Spatial Electric Load Forecasting Methods for Electric Utilities

Spatial Electric Load Forecasting Methodology

December 2007 © 2007 Quanta Technology LLC

Spatial Electric Load Forecasting

Methods for Electric Utilities

A report done for and with participation of the

Electric Energy Delivery Planning Consortium

By Quanta Technology LLC

H. Lee Willis, PE

Julio Romero Aguero, Ph.D.

December 2007

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Statement of Objectivity and Independence

Quanta Technology attests that it performed the work described herein an objective and impartial

manner and that it is reporting all results fully and in an unbiased, transparent manner. Conclusions

and recommendations are based on fact, comprehensive and balanced consideration of all issues,

unfettered by considerations other than the best interests of Quanta’s customer.

Quanta Technology, its officers and the members of its project team, have no business interest,

contractual obligation, or other ties to any issue, initiatives, products, services, or companies discussed

herein, that would limit their ability to perform this work in an unbiased manner, or to make objective

recommendations free of considerations beyond the best interests of the EDPC.

Statement and Disclosure Specific To Spatial

Electric Load Forecasting and This Project

Quanta Technology has experience with almost all of the software products discussed in this report,

having applied eight of them in past projects, or consulted on their use or design, or reviewed studies

involving their application.

In the 1970s, while at Houston Light and Power, Lee Willis, Senior Vice President, wrote the

program code that evolved into the program known as ELF-2 today. While at Westinghouse in the

1980s and into the early 1990s he guided the development of what is today ABB’s FORESITE. In

2005, at KEMA, he helped develop the electric version of USGS’s public-domain spatial forecast

program, SLEUTH-E.

Since being at Quanta, members of the project team including Willis, Phillips, and Romero-Aguero

and Le Xu have consulted to Itron on its application software including MetrixLT; to NETGroup on

improvements to its PowerGLF; and to Integral Analytics on the design of its LoadSEER and

Load@Risk programs. They have worked and continue to work closely with ESRI on GIS-based

workaround solutions to ELF-2, FORESITE and LoadSEER issues on behalf of several of its clients.

They provide roughly six workshops every year with EUCI, the T&D University, Distributech or

others on spatial load forecasting and planning.

In the past year Quanta Technology has completed utility consulting projects concerning the use or

purchase of ELF-2, FORESITE, INSITE, LoadSEER, MetrixLT, PowerGLF, PUCG-E, SERDIS and

SLEUTH-E. It currently has consulting retainers with six EDPC member utilities and two other

utilities for advice and support on their spatial forecasting applications, including a contract with Duke

Energy/Integral Analytics to provide continued development support on LoadSEER and with ComEd,

SCE, and MG&E to make improvements to their INSITE shareware and help them prepare for future

GIS-based applications.

This past and on-going work creates no obligation or bias to the team’s ability to perform this

project in a completely objective and unbiased manner, and in fact, has prepared Quanta to carry out

this project with a perspective and expertise gained from practical application and long experience.



Table of Contents

List of EDPC Members 2

Statement of Objectivity and Independence 3

1. Introduction 5

2. Spatial Electric Load Forecasting 6

2.1 Introduction 6

2.2 Spatial Load Forecasting 7

2.3 Spatial Forecasting Methods 8

2.4 Characteristics of Small Area Load Growth 11

2.4.1 “S-Curve Growth 11

2.4.2 Growth Characteristics and Area Size 12

2.4.3 The Most Important Point about Small Area Load Growth 13

2.5 Error and Accuracy in Spatial Forecasting 14

2.5.1 RMS and AA Statistical Measures 14

2.5.2 Spatial Correleation of Error 17

2.6 Spatial Forecast Methods and Algorithms 19

2.6.1 Trending Methods for Spatial Electric Load Forecasting 21

2.6.2 Land-Use Simulation Methods for Spatial Electric Load Forecasting 24

2.6.3 Re-development-based Simulation Methods 27

2.6.4 Hybrid Trending-Simulation Methods 30

3. Summaries of Commercially Available, Credible, 32

Tools for Spatial Electric Load Forecasting CARR-EL-2 32

ELF-2 33

FORESITE 34

INSITE 36

LoadSEER 37

MetrixLT 40

PowerGLF 41

PUCG/E 42

SERDIS 43

SLEUTH-E 44

4. Comparison of Forecasting Methods 46

5. Survey of Utilities Doing Spatial Forecasting 54

Bibliography and References 62



1. Introduction

This report covers an investigation of spatial electric load forecasting (T&D load forecasting)

methods carried out by Quanta Technology on behalf of the Electric Energy Delivery

Planning Consortium (EDPC). The scope of the work covered here, available to all EDPC

members, includes a technical literature search and meta-analysis of prior work in the field

and the current state of the science, a catalog and evaluations of methods and tools available

to the industry, comparison tests of eight commercially available load forecast tools, and a

survey of utilities using spatial load forecast methods. In addition, the five EDPC members

who sponsored this work each receive an additional report applying the results given here to

their specific needs and making recommendations with respect to their future planning

applications and software purchases.

As agreed during the EDPC meeting in Salt Lake City in December 2006, this project focuses

on commercially available, credible, spatial forecast methods. Taking those criteria in reverse

order: “spatial” means the tool applies a legitimate spatial, not just small area, forecasting

algorithm, or can be perhaps modified to do so without any re-programming. “Commercial”

means the tool is either a standard product of a software vendor/consulting company or

available in the public domain, and “credible” means that the tool is in active use by electric

utilities and that it uses a fully disclosed methodology (“no black boxes”) that has been

published in peer-reviewed technical journals. Research-grade programs such as those

developed in Portugal by Vladimiro Miranda or Korea by Prof. Lin Lui, no matter how

technically advanced and proven in academic tests, are not reported here unless they meet all

three criteria. Forecast methods meeting these criteria are compared on the basis of forecast

accuracy, representativeness, ease of use, data needs, and other salient qualities of the criteria

the EDPC defined in its December 2006 meeting. Users were surveyed about their

satisfaction with both the tool and the support they receive from their tools vendor, along the

lines of questions the five sponsor utilities helped Quanta develop.

Section 2 discusses basic concepts of spatial electric load forecasting and gives several

important points with regard to its application based on the meta-analysis done early in the

project. The Bibiliography/References section gives the list of technical papers and resources

developed in that task. Section 3 presents reviews of ten commercially available, credible,

electric load forecast programs, most of which successfully tested as spatial forecast methods.

Section 4 reviews the results of comparison tests of eight of those methods. Section 5

presents results of the utility user survey.



2. Spatial Electric Load Forecasting

2.1 Introduction

An electric utility’s customers are spread throughout its service territory but seldom

distributed evenly throughout that region. Figure 2-1 is an electric load map of a hypothetical

city, very similar to many in the United States, showing the typical pattern of geographic load

density in and around a large metropolitan area. In the core of the city, the downtown area has

very high load densities, the result of densely packed, high-rise commercial office and

residential development. Outlying suburban areas have a lower load density. But the load

density along major transportation corridors, even in the suburbs, is two to five times higher

than that and there can be office parks and major activity centers with near-downtown levels

of load density. Farther out from the urban core, in rural areas, load density is far lower still,

because homes and businesses are spread far apart. In some agricultural areas, however, load

density actually exceeds that of suburban areas, due to the intense loads of irrigation pumps,

as well as of oil pumps in petroleum fields.

This spatial pattern of electric demand defines the power delivery need – the overall job of the

utility’s T&D system regardless of where the power is generated or purchased, it must be

delivered to customers in that pattern in order to satisfy energy consumers’ needs.

Figure 2-1: Spatial pattern of electric load density for a medium sized city.



2.2 Spatial Load Forecasting

In order to plan an electric power delivery system, T&D planners need a map of electric load

density like that shown in Figure 1, but for the future, so they can plan where to put how much

capacity by the time when it will be needed. This map, or spatial forecast, must give that

where, how much, and when information in sufficient detail and with the required accuracy,

to permit effective planning of T&D facilities. The “where” information is what makes

spatial forecasting different from other types of forecasting. Information on future load

locations is needed in order to plan sites and routes for feeders, substations, and transmission

capacity in proportion to local needs throughout the system so that planners can anticipate,

plan for and justify these new, key elements of their growing future T&D infrastructure.

Basically, planners need a prediction of the future electric demand map like that shown in

Figure 2-1, with enough “where detail” to meet their planning needs, covering some key peak

time(s) in the future: a spatial load forecast. The spatial forecast depicted in Figure 2-2 shows

expected growth of the city in Figure 2-1 over the subsequent 20-year period. The growth

shown in the later map represents the demand that the utility’s T&D additions in this two-

decade period need to address in an efficient and orderly manner. Effective planning of the

T&D system requires that such information be taken into account, both to determine the least-

cost plan to meet future needs and to ensure that future demand can be met by the system as

planned.

Load in new areas Increase in density

1992 2012

Figure 2-2: Spatial load forecasts produce “where” information for T&D planning.



2.3 Spatial Forecasting Methodology

Area Size and Type

The “where” element in a spatial forecast is addressed by using some form of small area

forecast method: very simply, the utility service territory is divided into many, perhaps

thousands, of small areas, and a forecast of demand is done for each. Figure 2-3 shows the

two standard ways this spatial subdivision of area is done: by dividing the utility service area

into areas based on equipment – areas defined by substation or feeder service areas – or by

using a grid of uniformly shaped rectangular (usually square) areas.

.

Figure 2-3. Spatial load forecasts are accomplished by dividing the service territory into small areas,

either rectangular or square elements of a uniform grid or irregularly shaped areas, perhaps associated

with equipment service areas such as substations or feeders.

Table 2-1 lists the advantages and disadvantages of each approach as viewed overall by the

industry.

As part of their T&D planning, many electric and gas utilities perform small area or spatial

energy-use forecasts by equipment service area, for example forecasting future peak demands

on a substation-by-substation or feeder-by-feeder basis. Equipment service areas (e.g.,

substation areas) define the small areas. Using service areas of equipment like substations and

feeders to define the small areas for a T&D forecast is convenient but creates two issues. It is

convenient because the forecasts apply directly to planning purposes; a forecast by substation

area immediately tells a planner if the projected load in the substation’s current service area

will exceed its rated capacity, and that is perhaps the key aspect of load-related planning.

However, the equipment-area format creates two issues the utility must address carefully.



Table 2-1: Comparison Of Small Area Formats Used For Spatial Forecasting

Type of Area Typical Area Advantages Disadvantages

Equipment

Areas

Largest: Substation

Service Areas

1. Easy to relate directly to planning

method (feeder-area forecast relate

directly to feeder studies

1. As typically done, provides

insufficient spatial resolution to support

all planning functions.

2. Historical load data (feeder peak

loads) easy to come by and simple to

use in this format.

2. Incompatible with almost all types of

advanced land-use simulation forecast

algorithms.

3. Compatible with simple and

inexpensive algorithms such as

trending, etc.

3. Feeder or sub areas change size and

shape over time (load is transferred

back and forth).

Uniform

Squares

Largest: Square s 1 by

1 mile or 1 by 1 km

1. Usually provides more than enough

spatial resolution and detail for all

planning T&D needs

1.Data gathering, preparation, and

verification is generally more expensive

than for equipment areas

2. Uniform area size proves a big

advantage with some types of forecast

algorithms.

2. Incompatible withsimple and easy to

use forecast algorithms: basically only

simulation works well with it.

3. Works particularly well with

simulation-type methods and GIS-

based software systems

3. Requires procedure and effort to

relate small area forecasts on a square

basis to feeders, subs, etc.

Smallest: area served

by portion of a feeder

between two switches

(about 4-6 per feeder)

Smallest: 10-acre

squares (square areas

1/8 by 1/8 mile across

The first issue is that small areas defined by equipment areas change shape and size over time:

substation and feeder areas boundaries change from time to time because of load transfers

among them. Load transfers in the historical data distort analysis of historical trends, so most

forecasters using trending methods put some effort into correcting this data. “Removing”

load transfers from historical peak load data occupies as much as 80% of the time required to

apply some equipment-based forecast methods (Figure 2-4). Even then it is only partially

successful, because often knowledge of all past transfers between substations and feeders is

simply not available. There are some very innovative and clever methods to automatically

reduce error caused by load transfers, but load transfers remain a concern with regard to error

and cause near-excessive labor requirements in many equipment-based small area forecasts.

The second issue, just as important, is spatial resolution, which has to be addressed carefully

if an equipment-based small area forecast format is to be applied correctly and not “over

extended.” The problem here is the amount of “where” information contained in a forecast:

smaller small areas provide more detail as to where load is. How much information is needed,

and how much is provided by a forecast, is an important consideration.

Generally, load projections done on an equipment area basis provide enough locational

information to be useful for planning that equipment, but only at a high, “overview” level.

For example, load forecasts done on a substation-by substation area basis do support the study

of future substation capacity needs: they help identify when and by how much existing

stations may be overloaded, and give important clues to determining if and when additional

substations or substation capacity additions may be needed. However, a substation-by-

substation forecast does not provide all the “where” detail needed to support the study of

effective solutions to overload and capacity problems.

s

s



Figure 2-4: A large US electric utility (5 million connected meters, 5,200 feeders) spends roughly

1200 person-hours per year preparing data for and doing its annual distribution planning spatial load

forecast, which is performed using historical peak load data on an equipment-area (feeder) basis.

Adjustment and other issues related to load transfers accounts for 59% of the time its planners spend

on the forecast.

Generally, to determine the best plans to mitigate siting and capacity problems and to

minimize cost and maximize use of substation capacity, planners need to determine if and

how load transfers between substations (perhaps done with newly constructed feeder circuits

and switches) can be an effective part of the plan. This requires more spatial “where”

information than a substation-by-substation forecast will provide: it requires information on

where load is distributed within each substation area (Is growth expected on the west side of

the substation area, where there are few existing circuits and thus little capacity to transfer

loads to?). Since factors like this are often a key element of siting and planning new

substations (the new substation area will be “cut” from existing substation areas via new

circuits and load transfers), a higher spatial resolution – smaller area size – is needed.

Thus, a forecast done on a feeder-by-feeder basis will provide that required spatial detail for

substation planning. But in a similar vein, it will not provide all the information needed to

plan feeders in detail.

Experience and theory show that, overall, area size must be smaller – one fourth to one tenth

the average service area size of equipment being planned – for the forecast to support wholly

effective planning (Willis, 1983). Partly for this reason many spatial forecast methods use a

grid of small square areas of a size far smaller than substation or feeder service areas. Typical

area sizes used in grid methods are 10 to 40 acres (squares 1/8th

to 1/4th

mile per side)

although Duke Power, PacifiCorp, and several other utilities run their spatial forecast

algorithms at 1 acre resolution. Use of a grid assures sufficient spatial resolution, but is done

Gathering load data – 9%

Gathering data for new

customers – 10%

“Scrubbing” load

Transfers for

data history – 53%

Set up and

Forecast – 28%

Checking load

Transfer impact

In forecast – 6%

Post-forecast reviews,

Approval and use



mostly for two other reasons. First, there is considerably validity in the view that forecasting

by equipment service area ties the forecast and existing equipment together so much that it

distorts a true “unbiased solution” planning perspective – in a way putting the cart before the

horse as far as objectively evaluating how to best serve future changes in load density is

concerned. Second, a square grid is compatible with GIS and certain mapping systems,

making use of data in those formats easier, and certain types of forecast algorithms, mainly

land-use simulation methods, work best when the areas being analyzed are of constant size.

But while having the forecast in a different geographic format than the equipment may be

viewed as supporting objectivity in planning, it makes the forecast more difficult to relate to

existing system capabilities (“How do I determine if this forecast indicates whether the load in

the current substation area will in fact exceed its rated capacity?).

Regardless, many spatial forecast methods are in use around the world that work with either

of the two small area formats shown in Figure 3 and described in Table 1. Very recently,

GIS-based forecast methods that can simultaneously work with data input in both formats and

“cut and chop” their spatial forecast into either or both approaches have been developed.1

They work well, accepting data in mixed formats and producing forecasts that can be “output’

in either square grid or equipment-area bases. However, they require considerably more

computing resources and set up effort, to get them going.

2.4 Characteristic of Small Area Load Growth

2.4.1 “S”-Curve Growth

Figure 4 depicts what is often called an “S-curve,” a function of time is which a period of

intense slope is sandwiched between two periods of rather flat growth. Something like this

curve shape is almost always seen as the load history in any small area: more than any other

possible curve shape, the “S” curve depicts what load growth typically looks like at the small

area level (EPRI, Menge). The timing (when the period of most intense growth occurs) and

its characteristics (slope, duration, and final asymptote amount) will differ from one small

area to another. But to the point that it can be considered a general rule, a small area’s peak

load history will always look like this in some way.

The reason for this characteristic curve shape is that small areas “fill up.” Growth occurs

whenever construction is done in an area, initially when converting vacant land to developed

residential, commercial, or industrial areas, and perhaps again many decades later when older

areas of homes and industry are replaced with higher density commercial, etc. During the

period when development or re-development is intense in an area, growth is high, but once the

land available there is “built out,” growth moves on to other areas, and growth in that

particular area drops to nearly zero.

1 “Dual-format” spatial forecast algorithms run only within GIS systems like ESRI’s Arc-Info and GE’s

SmallWorld, using optional features within the basic GIS to manipulate and exchange data among different

SHAPE file formats.



Figure 2-5. The generic S-curve shape: a period of intense growth occurs between two periods of

relatively stable growth, one before and one after the small area “fills up with growth” in a relatively

short period.

This general characteristic is observable in any city in any part of the world. In Denver,

Calgary, Austin, London, Salt Lake, Raleigh, Mumbai, Cincinnati, Rabat, Philadelphia,

Adelaide, Jakarta, Xian, Boston or any other metro area, there are small areas within each city

that locals can identify that were “built out” in the 1950s, and others in the 1960s, or the

1970s, etc., up to those areas doing so now.

Thus, small areas always have intense, relatively brief, period(s) of growth, before and after

which the development and load growth are relatively stable (i.e., stagnant – little change) for

many years. A large region, say a city or a state, grows continually because there are always

other small areas ready to be developed – more vacant land for suburbs or more older, low-

value areas to be redeveloped.

2.4.2 Growth Character and Area Size

There is a further generalizable rule about “S-curve” growth behavior: the smaller the area,

the sharper the normalized S-curve shape (Willis and Northcote-Green, 1983; Engel, 1992,

Willis, 2002). Divide a large metro area into “really small” small areas, for example 4 acres

each squares 416 feet across) and the average 4-acre area might build out, whenever it does, in

only three years. That is the average time required for all the land parcels in a typical 4-acre

vacant area on the outskirts of a city to go from nearly vacant to nearly fully developed.

But do the same study of the same region at a 1 mile resolution – take “small” areas of 640

acres each and ask what the average “build out time” for them was and is – and the average

growth period will be 10 to 20 years. Area sizes in between will have growth periods in

between.

Time

MV

A



Figure 2-6. Normalized load curve shape is sharper for smaller areas. A 4-acre area might build out

in only 3 years, the average square mile (640 acres) could take 20 years.

Larger areas still would have “S” curves so mild that over a period of even ten years their load

growth trend might look like very much like a nearly straight line. Regardless, this type of

“S-curve behavior” at the small area level – both the fact that it is nearly a universal rule and

that S-curves are sharper and most distinct in small areas, is addressed in nearly all spatial

electric load forecasting methods in some way, and used to big advantage in the better ones.

2.4.3 The Most Important Point About Small Area Load Growth

Development in any particular small area occurs because of events, forces and factors that occur

somewhere else. A new sub-division is built because there is a regional demand for more

housing fueled by employment growth somewhere else. Shopping centers are built because

there is an increasing population in the region, and an unsatisfactory ratio of retail space to

housing in the areas nearby. New hospitals, schools, and other public infrastructure are built

in proportion and proximity to population increases overall. New industry and commercial

employment centers are built in response to economic factors spread throughout and even

outside of the region (Lowry, Willis and Northcote-Green 1983). Even re-development

follows this rule: Old mixed-use industrial areas near the urban core may be re-developed as

high-rise condos to satisfy demand for housing in that downtown core, but they develop

because there is a regional demand for housing and the only competing vacant growth areas

are so far away that they create a significant economic lost-opportunity cost (Haining).

The growth of any and every small area growth is linked to causes and forces located

elsewhere in the region – some nearby, others far away, but with few exceptions, all generated

somewhere else and linked by demo-econometric interactions to the small area. The process

of small area growth is spatial, not local-specific. The growth of any particular small area,

then, cannot be understood or forecast well by looking at only data about it.

Time

MV

A

100% of whatever

the area builds out

to eventually 4-acre

area

40-acres

area 640-acres

area



2. 5 Error and Accuracy in Spatial Forecasts

The fundamental point to keep in mind when measuring the accuracy of spatial forecast

methods is that their forecasts are done to determine load by location, so one wants a metric

that measures error in location, as well as in the amount of load.

2.5.1 RMS and AA Statistical Measures

The most obvious way to measure the error of a small area load forecast is to take the RMS

(root mean square) or AA (average absolute value) of the set of individual small area errors.

Given N small areas, for example feeders, indexed by n є [1 – N],

where An = actual load of area n

Fn = forecast load of area n

En = An-Fn = forecast error

Then, RMS % = (Σ (En)/N) / Σ(An)/N /100 [1]

AA% = RMS = Σ (|En|N)/ Σ(An)/N / 100 [2]

This is a useful error metric. Some planners misinterpret the accuracy and error discussions

in Willis, 2002, and assume these are not useful. That is not the case: RMS and AA are

useful, but they do not tell nearly the whole story, and, used alone, can mislead a planner.

For example, if the small areas are all 5170 feeders of, say, the Big State Electric system, and

RMS error is 15.2% and AA is 14.7%, this tells planners something useful. Their forecasts are

roughly 15% inaccurate when it comes to forecasting future feeder loads. Since RMS only

slightly greater than AA, the method makes relatively few big mistakes those much greater

than its average: it is fairly dependable, always being consistently in that 15% range of error.

Several comments are in order. First, consider that the error is roughly 15% of the entire load

(not just the load growth), and that this is from a forecast only a year or two out in a system

growing at perhaps something like 1.7% annually – for an average of about 5% growth in or

three years. Thus, the average error in forecasting a feeder’s future load growth only a few

years ahead is three times the average amount of growth.2

That does not seem to be good forecasting by any standards (it isn’t, but it’s not as bad as it

seems, either). Regardless, this conclusion is mathematically correct. Error calculated this

way is about 300% of growth. But this does not tell the whole story, and might mislead

planners into thinking that the forecast is unuseable.

2 Actual error levels for this case are: AA = 280% and RMS = 310% of average growth



Error estimates based on growth

Before going on, it is best to alter the formulae above to measure error only on the basis of

growth, not total feeder load. Given N small areas, for example feeders, each indexed by

n є [1 – N],

where ΔAn = actual load growth of area n

ΔFn = forecast load growth of area n

ΔEn = ΔAn-ΔFn = growth forecast error

Then, RMS % = (Σ (ΔEn)/N) / Σ(ΔAn)/N /100 [3]

AA% = RMS = Σ (|ΔEn|N)/ Σ(ΔAn)/N / 100 [4]

This change is an improvement in rigor, although error measured this way in this example

does to 280 and 310% respectively. But despite this, these formulae will still mislead

planners a bit. They do not correct the fundamental problem with this type of error measure –

that it does not tell the whole story about the type of errors taking place in the forecast.

Spatial Correlation of Growth

Suppose one does a slightly more comprehensive analysis of growth in the ComEd system, by

first defining something called a switchable neighborhood:

Switchable neighborhood for feeder n = all feeders to and from which load

can be transferred from feeder n

In other words, this is the group of feeders around the feeder we are studying, a larger area of

the system that contains feeder n. Define:

ΔGAn = actual load growth of area n’s switchable neighborhood

ΔGFn = forecast load growth of area n’s switchable neighborhood

ΔGEn = ΔAn-ΔFn = forecast error in projecting this growth

Then, RMS % = (Σ (ΔGEn)/N) / Σ(ΔGAn)/N /100 [3]

AA% = RMS = Σ (|ΔGEn|N)/ Σ(ΔGAn)/N / 100 [4]

In the case discussed here, error drops from 280% and 310% to 33% and 34% respectively

when looked at within “switchable neighborhoods. There is both a remarkable reduction in

error, and a noteworthy change because the higher original value (RMS) drops more.



There are two reasons for this dramatic change in values, one specific to all feeder-by-feeder

load forecasts, and one a general lesson in load forecasting.

First, the feeder-forecast specific lesson. In any distribution system, load is often transferred

among feeders from time to time to balance feeder loading, or to allow easier maintenance, or

simply to improve efficiency or operations in some way. This apparently random process

(from the standpoint of load growth analysis) happens to many feeders.

An important detail: this discussion is not referring to forecast errors caused by load transfers

done in the past. Attempts are always made to “correct” load histories used in any analysis

for past load growth. That is a messy and frustrating effort that can consume great amounts of

time (see Figure 2.4). It is assumed here that that all transfer-correction work was done well

and that the impact of load transfers is not an issue in this discussion.

But what is an issue is on-going load transfers. An, the actual feeder load, reflects load

transfers made over the three-year period from when the forecast was made to when it is being

compared for accuracy. The data history (up to the time of the forecast) might have been

“scrubbed” of all past load transfers, but the subsequent changes due to load transfers are not

included in Fn although they are included in the actual feeder loads, An.

Further, where would load transfers be most likely to occur? They would most often take

place as transfers of load from highly loaded (growing) feeders to neighboring feeders that are

not as highly loaded. Even further, the average load transfer is very likely to be somewhat

more than 5% (roughly the amount of load growth, at 1.7%, that occurs over three years).

Load transfers are usually at least 8 to 10% on most feeder systems.

And finally, a transfer is seen as an error twice in the data: once as a deviation at the “from”

feeder, and once as a deviation at the “to” feeder.

Thus, to summarize: after any forecast is made, switching continues as an on-going operations

tool, usually with the amounts switched being larger than the average amount of load growth,

with a heavy bias toward those areas where there is a lot of load growth, and with the

deviations in load showing up in data in a way that creates a statistical “double whammy” to

the error measure.

In fact, a majority of “error” in most feeder-by-feeder forecasts where error is computed in the

manner done here (equations 3 and 4), is due to mismatches caused by switching done

subsequent to the forecasting. Further, the cause of these errors is biased toward high growth

feeders, affecting RMS more than AA.

Looking at the error from the standpoint of switchable feeder neighborhood around any one

feeder greatly reduces (almost eliminates) this issue, and consequently greatly reduces the

error measure. The error is still large, but planners now have an idea of how dependable this

forecast is as a planning guide: more than 2/3 of the time it will provide a useful indication of

where load will occur, at least to the point that load transfers alone can take care of the

mistakes.



2.5.2 Spatial Correlation of Error

There is another, more general, and ultimately, more meaningful lesson with regard to spatial

correlation of forecast error (how it aggregates or relates one area to another). Forecast error

can be viewed, and thought of, as locational, not magnitudinal. Instead of saying “The

forecast method misforecast the average load of a small area by J%,” one can say something

like “The forecast method misforecast the location where the average MW of growth occurred

by K miles”: it forecast most growing loads correctly, but just got their locations wrong.

This is not a perfect way to look at error, either. But thinking about spatial forecast errors in

this purely spatial (locational) manner helps a person understand what is happening with error

in a spatial forecast, and ultimately to apply T&D load forecasts in a better way.

For the moment, suppose that in this example the N areas are not feeders, but instead, square

areas, each 1 mile wide, in a grid covering the same large city. Load transfers would not have

been an issue at all: load is never transferred from one square mile to another: a Walmart or a

new Del Webb housing development stays put, once built. We can forget all about transfers.

For the sake of discussion here, assume for a moment that the forecast method that generated

these forecasts has a locational error of about one mile in this same short time frame we have

been discussing: three years ahead load growth is on average forecast to within 1 mile of

where it subsequently really does develop 90% of the time: sometimes the method is more

than a mile off, other times closer, but on average, the particular forecast gets within 1 mile

90% of the time.

A circle of 1 mile radius has an area of 3.15 square miles. The forecast method is 90% certain

to forecast load growth within that area. Since the 1-mile square small areas we have asked it

to forecast are roughly 1/3 that size, as a very rough approximation, one might expect that this

method would be accurate to the 1-mile area size about 30% of the time – meaning error

would be 70%. This approximation actually overestimates error slightly for a variety of

reasons beyond the scope of, and not central to, the discussion and point being made here. A

method that gave the 33% and 34% figures when evaluated on switchable feeder

neighborhoods (as discussed earlier) would give about AA=48% and RMS=52% error on

square miles, rather than this estimate of around 70%. These 48% - 52% values are higher

than the 33% - 34% values for switchable neighborhoods because square miles are on average

a bit smaller than the switchable neighborhoods or feeders: it is more difficult to forecast load

to smaller areas and so spatial error will be greater, something we will now look at in detail.

Now, suppose that one looks at this same forecast, but from the standpoint of how accurate it

looks to be on a 2 mile by 2 mile square basis. One can do so by just adding up four adjacent

square miles into blocks of 2 x 2. Now, the “small areas” are four times the size. The error

circle is only about ¾ of one of these areas. Our estimated error (the approximation used

above to get 70%) would be cut in half, perhaps to something like 33% and in actuality error

would drop from 48% and 52% to about 25% and 28%. 3

3 This approximation is derived as ¾ times 90% = 68% error is 32%.



One can look at this effect in any actual load

forecast error test by simply adding up the errors,

the En, of blocks of four adjacent 1-mile small

areas and re-computing the error measures. Very

often, one will get a situation like that shown in

Figure 2-7. Errors within any block of four

partially “cancel out,” and of course, the

denominator (total amount of load) is larger, so

error percentage tends to be much smaller.

Repeat this again to create even larger blocks,

and error percentage drops further. One can

continue to do this until one is at the system

level, essentially asking “How good did this

method do at forecasting that the load growth

would occur somewhere on our system?”

Figure 2-8, solid line, shows the results of this type of analysis for a forecast of a large cities

growth for 2004-2007, the forecast discussed above. At a 40-acre resolution (square small

areas ¼ mile wide) error is about 100%. At a mile, RMS error is about 50% – the method is

as likely as not to get the load growth in a square mile accurate. At the feeder level it’s about

30% error, and at the substation level (areas of 12-25 square miles in size) around 12%

accurate.

Figure 2-8 Error computed for various spatial resolutions, as done here, to show how accurate a

spatial forecast was at various levels of system planning. Lines here show error characteristics for two

particular types of forecast methods, both forecasting three years apart. See text for details.

+ + + - + +

+ + + - +

+ - + + -

- + - + - +

+ + - - - +

- + + - +

Figure 2-7. Here, size of plus or minus

sign indicates amount of over or under

forecast in a small area. Much of the

error “cancels out” when areas are added

into blocks of four.

1 10 40 100 1000 10000 100000 Million 10 million

Mile Feeder Substation Chicago

Size of Area - Acres

500%

50%

5%

.5%

RM

S E

rro

r =

%



The particular method being discussed (a small area, but not a spatial, trending method – see

Section 2.4) is not among the best available. The dotted line shows the 4-year ahead error of a

forecast using ABB’s FORESITE for the same city, done a few years earlier. The advantage

is very significant (the error scale is on a log basis). The two plotted lines cross due to the

spatial fitting error, something that will be discussed in Section 2.5.

2.6 Spatial Forecast Methods and Algorithms

There are more than 60 different computerized small area electric load/T&D planning forecast

methods that have been used and documented in the last 40 years. By “method,” we mean a

basic analytical approach to performing the forecast: “Let’s extrapolate load histories on a

substation by substation basis using polynomial curve fit solved by multiple regression,”

“Let’s model growth as moving from one area to another over time by fitting a spatial

dispersion function to feeder load histories using a spatially symmetric, temporally causal,

auto-regressive function of peak demand,” “Let’s study land-use patterns and municipal plans

and estimate future load from them.”). For any one method, there may be several different

algorithms or computer code sets in use to apply it: For example, there are easily more than a

dozen ways that extrapolation of substation and feeder peak load histories have been done,

each a distinctively different way of implementing the basic concept.

Despite the wide variety of approaches, all fall into three basic types of method, listed along

with salient characteristics in Table 2-2: trending, simulation, or hybrid trending-simulation

methods. But before discussing the types of method, there is one key aspect to address:

All spatial forecasts are small area forecasts, but all small area forecasts are

not spatial forecasts. A spatial forecast is a small area forecast in which

every area was consistently forecast, one to the other, so they are part of a

coordinated region-wide picture of future load growth, including how growth

interacts from one area to another and “moves” spatially over time.

To understand this distinction, it is useful to consider the most obvious small area load

forecast approach, one that many people immediately consider when first approaching the

need to do a T&D forecast – trending of local area peak demands using some sort of curve

fitting to past load history in each area. In this method, historical data on weather-adjusted

peak demands for each small area (perhaps its peak loads for the past ten years) is

extrapolated into the future using some sort of curve fitting method. This produces a small

area by small area forecast but not a spatial forecast. Each individual small area’s peak

demand projection is based on data only about that particular small area, with no

consideration given to its interaction with its neighbors, the pattern of regional growth, or to

the use of information that a statistical analysis of how growth varies among small areas could

provide. Basically, this is a set of N individual small area forecasts: no attempt has been made

to analyze or forecast this set of small area load histories as a whole; neither a coordinated

forecast of the region or a forecast in which information on growth influences from outside

each small area has been considered in its forecast.



Perhaps the best way to understand why spatial trending methods forecast so much better than

mere small area methods is to look at the information available to the forecast algorithm and

how it is used. Any trending method has a set of small area load histories to analyze and

forecast – perhaps several thousand load histories for several thousand feeder areas in a large

power system. An individual polynomial curve fit extrapolation of each small area’s load

history uses only the information on that particular small area’s load history to forecast its

future trend. Out of perhaps several thousand small areas load histories, it uses only a tiny

fraction, of the data and information to do this forecast. Yes, that particular data is perhaps

the most relevant information about that particular small area, but by ignoring the data of all

the other small areas this simple small-area extrapolation method throws away information

that could be useful.

Table 2-2: Comparison of Basic Categories of Load Forecast Method

Factor Trending Simulation Hybrid

Basic Idea Behind

the Forecast

Extrapolate past trend in weather-

corrected annual peak load growth

into the future on a small area basis

Model the processes driving growth:

(1) Spatial expansion of mankind's

use of land -- new homes being built,

etc., 2) changes in usage of

electricity and other energy sources as

it is expected to occur into the future,

Both on a small area basis

Mix trending and simulation in some

way that hopefully combines more of

the advantages of each than the

disadvantages of each

Type of Area

Format Used

Typically applied on an equiment-area

basis since load histories are in that

format.

Almost universally applied on a grid

basis because of compatibility with

land-use algorithms.

Has been applied in either equipment

area or grid basis.

Typical

Algorithms

Simplest: polynomial curve fit to past

load histories (not a spatial method);

xxxxxxxxxxxxxxxxxxxxxxxxxxxx

Most effective: heirarchical recursive

semisoidal curve extrpolation on a

small area basis, contorlled by a

spatial growth statistical and pattern

recognition analysis

Usually some combination of an

"urban model" simualtion of land-use

changes a end-use/rate class load

curve model of evolving per capita

consumption patterns

Various algorithms that meld land-use

and historical trend analysis:

successful proven methods used

spatial trending guided by long-term

land use change data

Short-range

accuracy for T&D

planning

Fair to outstanding

depending on method and

the degree of success in correcting

load transfers in historical peak data

Fair to very good

depending on data and

accuracy of calibration to the

base year/historical data

Good to outstanding

depending on data and

accuracy of calibration to the

base year/historical data

Long-range

usefulness for

T&D planning

Extremely poor to

"not quite satisfactory"

depending on method

Good to excellent

depending on method

Good to very good

depending on method

Useful for

Integrated

Resource

Planning too?

No

Labor involved Low to high depending on attention

given to load transfers

High to extremely high Medium to extremely high

Yes, depending on the type of end-use load

curve model used, perhaps extremely useful



The downfall of small area (as opposed to spatial) trending methods is the vacant area, an

undeveloped small area that has little or no development and thus no established history to

provide a base for the extrapolation. Planners may suspect, even know, that it will develop in

the future, but what is a simple extrapolation algorithm to do? The load history is zero or near

that. Extrapolate that and one gets zero. The load history data in this area has no information,

and so an extrapolation is left without any basis for accurate forecasting.

Yet there is information, plenty of information in most cases, about what to expect in that

vacant area, in the load histories of other small areas. Maybe this small area will grow

roughly along the trend followed by hundreds of small areas of about its size and type and

situation before it. (Why wouldn’t it?) So, what does the growth history of other small areas

about like this one, that grew in the recent past, look like (how sharp, how high, are their

average S-curve?). When does an area like this start growing as compared to when areas

nearby it “build out” and stop growing? This type of information, gleaned from the study of

other small areas, is used in spatial electric load forecast methods to improve the forecast of

every small area. Spatial forecast methods reduce small area forecast error by over half.

While this discussion will touch on both small area and spatial forecast methods, only spatial

methods are recommended for T&D planning. Table 2 lists some salient characteristics for

the three major categories of small area load forecast.

2.6.1 Trending Methods for Spatial Electric Load Forecasting

Trending methods extrapolate recent trends in small-area load growth into the future. As

discussed earlier, the most obvious small area trending approach, and perhaps the simplest, is

to extrapolate the trend of annual peak demand growth in each small area (feeder or

substation) over the past five to ten years into the future using an extrapolation method like

multiple regression polynomial curve fit or pattern template matching. A wide variety of

computer programs, each with slight variations on this theme, were developed beginning in

the 1970s and have been applied in this manner worldwide, with several new ones of this type

surfacing each year; this is the approach most people take when confronting the problem for

the first time, and every year a few who have done little or no research on prior work re-create

this approach. Forecasting with a curve-fit that forces all load histories to be some form of

“S-curve” (see section 2.4) reduces error by half compared to curve fit of third or second order

polynomials. Regardless, T&D planners should understand that any approach that only

serially extrapolates the peak demand history of each small area individually is not a spatial

forecast method and it is not very accurate or useful as a planning tool, as was discussed

earlier on this page.



Spatial trending methods

The simplest spatial trending method is without a doubt multi-area Markov Regression, which

simultaneously fits polynomials to a number of neighboring small area load histories in one

computation, while putting constraints on their joint growth pattern derived from a prior

analysis of how growth “moves” from one small area to another (Willis, Powell, Tram). This

reduces error by about half compared to the best curve fitting on an individual small area

basis. It also increases algorithm complexity a great deal, and computation time and

sensitivity to data error and round-off error for the curve fitting by up five orders of

magnitude (i.e., from insignificant to burdensome, even at today’s processor speeds), so this

method was not considered practical and was never widely used.

The first practical spatial trending method used hierarchical resolution to infer small area

growth timing (Willis and Northcote-Green, 1982), usually applied in a recursive (one within

another) manner. Its “trick” was simple: when the algorithm encountered a small area with so

little load history that it could not dependably forecast a trend, it added that small area’s load

history together with that of several other nearby small areas into a “block.” Very likely this

bigger block has an extrapolate-able load history: the algorithm trends that. Once that bigger

block is extrapolated, the method then addresses how the small areas inside it will grow: those

with an “extrapolatable load history” are forecast. Their sum is subtracted from the trend for

the larger area, and that is assigned as the inferred load growth for the small area that had no

load history.

Of course, if the bigger block has insufficient load history, too, the algorithm does the same

again: four blocks into a mega block, etc., until it finds load histories sufficient to trend. In

this way, this first practical spatial electric load forecast method was able to infer when

growth would occur in small areas with little or no load history: they were basically the small

areas that would have to “eventually grow” in order to continue long-term trends in the larger

areas containing them, once those small areas now growing there had built out. This

hierarchical also improved forecasting in areas that had load histories, too, not just in vacant

areas, so programs were developed that applied the hierarchical method to all small areas,

regardless of load history. Forecast accuracy improved further.

HRGF Methods

The most accurate modern trending methods use some form of that hierarchical blocking

approach combined with S-curve fitting and a set of rules that represent S-curve shapes as

being sharper the smaller the “block size” – what is now called the HRGF (hierarchical,

recursive Gompertz-curve fit) method. This method was first commercialized by Carrington

(Carrington, 1988 – See CARR-EL discussion in Section 3), although it was used prior to that

in a set of load forecasting “shareware” developed in South America in the mid 1980s (see



INSITE discussion in Section 3). Regardless, the method combines hierarchical recursive

trending (Willis and Northcote-Green, 1982) – basically the blocking “trick” described above

– with Gompertz (“S-curve) fitting (Willis, Powell, Tram, 1984) – two trending methods

largely abandoned in the US in the mid 1980s in favor of simulation approaches.

A commercial computer program using this approach, CARR-EL, saw wide commercial use

in Europe and Africa beginning in the late 1980s. SERDIS (Eastern Europe – see Section 3

for more details) also uses the HRGF concept but with a type of simultaneous curve fitting

similar to Markov regression. Quanta has implemented the concept using expert-system rules

instead of numerical methods (again, see INSITE, section 3). Despite different algorithms, all

three programs not only produce nearly identical error statistics in side by side tests, they do

so by making roughly the same “mistakes” in forecasting the same small areas, despite using

completely different bases (numerical, expert system), and represent the best level of forecast

seen from any trending approaches. HRGF trending also provides a very good platform for a

hybrid forecast algorithm, something that will be discussed later.

These spatial HRGF methods are all computationally intense compared to small area trending

methods like individual small area polynomial curve fitting and even to some simulation

methods. HRGF begins with an analysis and comparison of small area load histories in which

the algorithm builds a database of rules (if using AI expert-system methods) or statistics (if

using numerical methods) of how small area growth “looks” at the small area level: average

and extreme load history shapes, spatial correlation of growth, growth behavior patterns, etc.

It compares every small area growth history to others around it, looking for patterns of growth

among sets or groups of small areas, and/or by condition, etc: developing rules or numerical

constraints that apparently applied in the past that they can apply in the future. HRGF

algorithms do this for all small area resolutions possible within the spatial context: from the

base resolution (smallest small area size) and for blocks or groups of larger and larger areas.

Advantages and Disadvantages of Trending

While spatial trending methods and HRGF in particular require quite complex algorithms, all

trending methods are simple to apply: just input the historical data and run the program. It

requires only historical load data (e.g., peak demand data on feeders for the past ten years)

which nearly all planners have readily at hand,, and it can be applied on an equipment area

basis (it directly forecasts feeder loads, or substation loads, etc., since it is extrapolating load

histories for those). This, and the need that all trending methods share for data that is always

relatively easy to obtain (compared to simulation), are the key advantages of trending.

The chief disadvantage of small area trending methods is that they are not good for much

more than short-range forecasting. First, they simply do not forecast well over periods longer

than perhaps five years at the most. Over periods of more than five years, the factors driving

and controlling growth change. This is a fundamental barrier to trending. Since information



on future driving and controlling factors different from today’s is not embodied in historical

load data, no algorithm, no matter how smart, can infer what will happen.

It is the ability to model such changes over the long-range, so that better forecasts can be

produced, that makes simulation approaches so much better in the five year and further ahead

timeframe. In that time frame “accuracy” in the sense of error in estimating future load has

little relevance: what is needed is an ability to represent specific scenarios of changed future

driving and controlling factors. The best trending methods have only very limited

representativeness and are thus not suitable for long-range studies. Simulation is ideal.

Several commercially available programs that implement spatial trending methods are

described in Section 3. The most popular in the Americas are Itron’s MetrixLT and versions

of the shareware INSITE. Internationally, CARR-EL and SERDIS are also widely used.

2.6.2 Land-Use Simulation Methods for Spatial Electric Load Forecasting

Simulation methods apply some type of land-use change model to forecast how customer type

and density will change on a small area basis over time, then translate forecasted small area

customer type and density to electric load on a small area basis using “MV-90” type load

research data and load curves to produce a small-area projection of future electric load.

Basically, these models try to predict how driving and controlling factors in the local

economy, demography, and geography will combine to affect the pattern of small area

growth. Thus, these approaches are potentially very good at scenario representativeness for

long-range planning.

The Lowry Urban Model Land-Use Simulation Approach

Until recently, almost all simulation-based spatial electric load forecast methods used some

form of Lowry urban model (Lowry, 1964). The Lowry approach “scores” each small area

for how likely it is to develop residential, retail, commercial, or industrial development, and

then allocates compatible amounts of each land use class to small areas with the highest scores

on a year by year basis into the future.

The Lowry model approach is often called a linear urban model because the base functions

that produce the small area scores are linear functions of small-area data and various distances

computed by the program (distance to downtown, distance to the nearest shopping center,

etc.). The computer algorithms used are occasionally referred to as a Lowry-Garin or Garin-

Lowry models: the original Lowry model was conceptual, not numerical; Garin generalized it

to a set of matrix computations easily implemented by computer (Garin, 1966).

Developed in the mid 1960s and widely used for metropolitan planning in many industries,

not just electric, the Lowry approach assumes that growth in a region is ultimately driven by

increases of employment: regional land use change is driven by growth centered at major



employment centers (often called urban activity centers, or urban poles) such as the

downtown core of a city or a heavy industrial area near a port area, etc. As employment in

these activity centers grows, demand for new homes, etc., also grows, centered geographically

at those locations: the Lowry model interprets this as a spatial demand for growth – residential

areas nearer growing employment centers are in more demand than those far away.

Where residential growth ends up occurring, however, depends on of local characteristics in

and around each small area. That evaluation focuses on factors such as: it must have a local

profile matching “would make a good residential area, etc.; it should be close but not to close

to major transportation corridors; near but not too near existing retail shopping; near schools,

etc. These are all qualities that are often called “surround” or “proximity” factors because

they deal with factors very close to but often not in a small area.

Assessment of these factors for each area determines an area’s land-use suitability scores:

numerical measures of how suitable it is for residential development, for retail, for

commercial offices, for light manufacturing, for heavy industry. Each is added to an “urban

pole” factor that scores how close the small area is to the basic employment centers, based on

its and their locations. The combined score ultimately control if, how, what, when and

particularly where land use growth is forecast to occur Small areas with the highest combined

urban-pole and residential local factors score sum for residential will be those that are

modeled as seeing residential growth. The resulting model of land use change balances the

spatial demand and small area supply to predict land use change on a small area basis.

In electric utility applications the computer program then converts forecasted land use to

electric load on a small area by small area basis, using typical electric load densities for each

land-use class along with the forecasted amounts of land-use development there (so many

houses use this much power, so many acres of offices will use that much, etc.). A good

review of the Lowry approach specific to utilities in available in Willis, 2002. A slightly

more accessible discussion of the Lowry concept and an explanation of how it works, is in

Wikipedia (http://en.wikipedia.org/wiki/Land_use_forecasting).

Spatial electric load forecast methods using the linear Lowry approach established an

excellent track record in urban planning, highway, water and sewer, and school/public

transportation planning in the 1960s and 1970s. They were developed for electric power

planning in the mid 1970s and 1980s (Willis et al, 1977, Brooks and Northcote-Green, 1978,

Willis and Gregg, 1978, Fischer 1980, Ramasamy, 1988).

In the period 1980 to 1990, the Lowry approach, implanted as several different computer

programs, dominated T&D load forecasting at major electric utilities in North America. A

group of utilities in Texas jointly developed and used this method (Willis et al, 1977, Fischer,

1980); the Canadian Electric Association developed a tool for use by its members (CEA,

1982); and a number of utilities in the US used computer programs called DLF (Scott and

http://en.wikipedia.org/wiki/Land_use_forecasting



Scott, now Advantica), or SLF from Westinghouse AST (now ABB).4 All these Lowry-based

forecast methods established a good track record, particularly with utilities serving fast-

growing metropolitan areas, including Austin, Houston, Dallas-Ft. Worth, Phoenix, Tampa,

Orlando, Atlanta, Denver, Calgary, Portland, Salt Lake and other similar metropolises.

At least two spatial forecast programs using a Lowry-derived model are commercially

available in the Americas: ELF-2, a forecast service using simulation methodology first

developed by utilities in Texas in the 1970s-1980s, and FORESITE from ABB, a derivative of

the original Westinghouse SLF program from the 1980s, it being an evolutionary

improvement of the same programs that led to ELF-2). Internationally, there is also PUCG-E

(roughly translated from the Hindi, meaning “Peripheral and Urban Congestion Growth-

Energy) from Tata in India (See Section 3). However, Lowry concepts, if not the model

itself, are used in many other T&D electric forecast models.

Modeling Redevelopment Rather Than Just Greenfield Growth

The Lowry approach was designed around the concept of “Greenfield” growth forecasting: to

predict if and how vacant land, usually on the edges of large cities, would develop into

suburbs, office parks, shopping malls, etc. Set up and calibrated well, a Lowry model is quite

accurate at predicting major growth trends occurring in vacant, peripheral areas on the

outskirts of a metropolitan regions growing under any reasonably free-market system of land

purchase and development.5 During the 1970s and 80s, the majority of users of the Lowry

approach, whether in public and urban planning, road and water planning, or electric and gas

utility planning, focused on this type of growth and were well satisfied with its results.

But the Lowry approach does not do nearly as good a job at forecasting “Brownfield” growth:

re-development of existing land-uses such as when older commercial areas redevelop as high-

rise commercial, long-quiet light industrial areas make transitions to mid rise offices and

condos, and when there is a slow, scattered, but steady replacement of two-story commercial

with five story, etc, in developed parts of a city. The basic Lowry approach both does not

consider the root causes of re-development (at some point the commuting time is worth more

than the cost tearing down the existing development in the inner city to make it “developable”

again) nor have the ability to balance the factors shaping redevelopment (land value of current

development vs. commuting time) against those for Greenfield growth.

The basic issue is depicted in Figure 2-9 with a simple but illustrative four-class land-use

transition matrix. Lowry-based models develop data needed to analyze only those transitions 4 Through several evolutionary changes, this formed the foundation for ABB’s current offering, FORESITE (see

Section 3).

5 The Lowry approach is not good at forecasting metropolitan growth in cases of tight government central

planning and rigid control of growth, but then neither is any other method: central plans notwithstanding, actual

results there depend on politics more than economics or logic. No method seems capable of forecasting that.



from vacant to a developed land use class (shown shaded in the leftmost matrix). What are

often called above-diagonal re-development models (Haining, middle diagram) consider all

those transitions where development moves from any land use (vacant or developed) to a land

use with a greater nominal economic value (defined as its share of the regional or local

econometric aggregate).

Figure 2-9. Lowry models (right) examine only transitions from vacant to some other land use state.

Above-diagonal models (middle) assess all transitions from lower to higher value development. Full

matrix approaches (right) take a substantially different approach. They assess all transitions including

“stay the same” (diagonal elements) and then apply them in a cellular automat or similar model venue.

Full-matrix development models (rightmost diagram in Figure 2.9) consider all possible

transitions including those on the diagonal (vacant to vacant, residential to residential). This

last may seem an unusual step but is generally regarded as the reason for their superiority:

they proactively evaluate continuation of the status quo in a small area, not just transitions to

something else.

Several attempts to modify the Lowry-Garin approach to include above-diagonal transitions

(middle diagram) were done in the late 1990s, but proved unsatisfactory (Lodi and Dramian,

Dramian and Colter, Willis 2002). The causes of re-development – the “machinery” at work –

are outside the context of a Lowry models. Modeling redevelopment requires gathering,

analyzing, and using completely different local and regional factors. Thus, a modified Lowry-

Garin algorithm may “look” at transitions other than Greenfield, but it doesn’t sufficiently

analyze the forces involved to predict them well.

2.6.3 Re-development-based Simulation Methods

In most metro areas in the US, including quite a few of those cities where Lowry-based

models were popular planning tools in the 70s and 80s, re-development is now a significant

part of regional growth, and in some cases it is a majority. This is one of several reasons why

Vacant

Residential

Commercial

Industrial

Vac

ant

Res

iden

tial

Com

mer

cial

Indu

strial

New Land Use

Pri

or

La

nd

Us

e Vacant

Residential

Commercial

Industrial

Vac

ant

Res

iden

tial

Com

mer

cial

Indu

strial

New Land Use

Vacant

Residential

Commercial

Industrial

Vac

ant

Res

iden

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Com

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Indu

strial

New Land Use



the Lowry approach fell out of favor in the late nineties.6 As a result, it has been replaced or

augmented by newer growth simulation methods during the last ten years, not just in the

electric industry, but for other infrastructure and planning purposes (urban infrastructure,

highway, etc.) as well.

These newer methods do take a land use simulation approach, forecasting land use change on

a small area basis and using the forecast land use to infer future electric load. They also link

growth to regional employment change, and small-area growth to land use suitability based on

local factors like proximity to roads, etc. However, they do not use a Lowry approach.

Instead, the do at least two, and sometimes three, separate and distinct evaluations of all small

areas. One is vaguely similar to the Lowry model’s, basically a “what would you best be

when you grow up?” type analysis of land-use suitability on a small area basis, although it is

done in a different manner to make it compatible with the other “evaluation planes.”

Another, separate “evaluation plane” is a set of factors and analysis used to assemble a “is

there a positive net value to your conversion to a another land-use type?” which can take any

of several different forms (Haining, Lodi and Colter, Clark and Leonard, Willis 2002) but

always boils down to some analysis of land-value or the small area’s role or “share” to the

region “economical machine.” Finally, a few models (Haining; Willis, Stevie, Osterhus,

Skinner and Phillips) perform a detailed analysis of commuting time and its economic value

(or lost opportunity cost).7

At least four computer programs using variations on these themes have proven successful at

simultaneously forecasting Greenfield and Brownfield growth and balancing one against the

other, although only two have been applied to electric forecasting:

- The SLEUTH model used by US Geologic Survey to predict how growth will

change flooding patterns in urban areas applies three different urban models

simultaneously, including one somewhat like a Lowry approach, and then

applies a rule-based system to determine which applies to which small areas,

building up a composite forecast in this way. The algorithm is sometimes called

a “Clark model.” (Clark and Leonard). See Section 3 for more detail on

SLEUTH, how it works, and how it was applied to a electric planning

application.

6 A larger reason is that most utilities including those serving major metro areas cut back significantly on

planning, particularly long-range T&D planning, and therefore lost the need for spatial forecast methods that had

good Representativeness capability in the beyond-five year period. However, the Lowry model’s inability to

forecast re-development explains why it is not popular now that there is a resurgence of interest in long-range

T&D planning and therefore in spatial forecasting methods.

7 For example, both Haining’s model as used for city planning in England and Scotland, and one optional version

of the LoadSEER program sponsored by Duke (see Section 3), run a “traffic load flow” based on predicted

residential land use and expected future highways and roads.



- Robert Haining (Cambridge University) developed a non-linear small area

regression model used for metro-area facilities planning in the UK that works

by computing spatial “re-development pressure” in inner-city areas and

balancing that against economic costs associated with commuting versus

developing cheaper vacant land on the outskirts of a metro area (Haining).

- Prof. Vladimiro Miranda (Portugal’s INESA) developed a cellular automata that

models land-use change on the basis of land value and commuting cost to

employment centers as well as suitability for particular types of land use. The

resulting research-grade spatial electric load forecast program was technically

successful but has not been commercialized (Miranda).

- Integral Analytics’ LoadSEER program uses an agent-based cellular automata

method. The algorithm combines the best elements of SLEUTH’s Clark model,

Miranda’s cellular automata, and Haining’s land-value/lost opportunity models

with a rule base from the INSITE HRGF expert system model. (An agent-

based approach is merely a way of organizing the multiple models that “run

simultaneously” in a somewhat more rigorous manner than done in SLEUTH.)

See Section 3’s discussion of LoadSEER for more detail.

All four are relatively new approaches but each established a good, if limited track record of

balancing Brownfield and Greenfield development: SLEUTH in applications to Baltimore,

Colorado Springs and San Francisco in studies for USGUS flood-plain assessment; Haining’s

method on Birmingham and Glasgow for public infrastructure (schools, police) planning,

Miranda’s on several cities in Portugal and Brazil for electric planning, and LoadSEER on

Cincinnati, Charlotte, and Washington DC for electric and DSM planning.

In addition, SLEUTH’s rural-area growth model has proven to be a breakthrough in

forecasting growth “just beyond” the edges of a metropolis. It is currently the most accurate

forecasting tool for land use change in sparsely populated areas, soundly beating the “reduced

dimension” rural forecast Lowry approach which had been the best available approach since

the 1990s (Willis, Finley, Buri) in direct comparison tests.

Regardless, all modern simulation methods, including the four discussed above, whether pure

Lowry approaches or newer algorithms, work with land use and customer data on a spatial

basis from within Geographic Information Systems (GIS) like GE’s Smallworld or ESRI’s

Arc-Info. All make heavy use of the spatial data and analysis features of those systems. For

electric planning, the “land use” data is generally obtained by “dumping” the utility’s

customer information system (CIS) data to small areas using the GIS, and by obtaining local

municipal utilities zoning data in GIS format, etc.

Simulation method fitting error. Traditional land-use based simulation methods for spatial

electric load forecasting methods often are not highly accurate in the one to three year ahead

timeframe. Many have a slight spatial fitting error (caused by difficulties in fitting small area



Figure 2-10: Many simulation methods (dotted line) suffer from a small amount of “fitting error”

when trying to explain feeder loads precisely. Although only a few percent of error, this is on the

order of the growth rate, so it makes them slightly less accurate than the best feeder trending methods

(solid line) at very short-range applications, as shown here in the results of direct tests of a feeder-

history HRGF trending method versus a well-set up and proven simulation method (FORESITE) on an

area of eastern Salt Lake City.

loads exactly with land use) that is typically equivalent to one-half to one year’s load growth.

Thus, it takes several years into the future before their fundamental accuracy advantage over

trending methods overcomes that initial mismatch, resulting in an overall better result (Figure

10). At least one modern land-use simulation program uses “filters” for stabilizing these

fitting errors to improve short-term forecast accuracy, borrowing techniques from HRGF

trending methods.

But the chief advantage of simulation is not short-range forecast accuracy. The best can do as

well or only a bit better than the best trending methods in the one to three year timeframe.

Simulation’s forte is longer range “forecast accuracy” -- representativeness in modeling

scenarios, as well as better communicability of their results: well-displayed maps of future

land use growth, etc., help “sell” plans that utilities want approved, much more than numbers

and mathematically fitting statistics can.

2.6.4 Hybrid Trending-Simulation Methods

Hybrid forecast methods are, strictly speaking, any forecast method that combines elements of

the trending and simulation approaches in an attempt to gain the advantages of both while

avoiding the disadvantages of either. To date, the only successful approaches from a practical

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standpoint have been methods that combine HRGF trending methods with limited amounts of

land-use simulation to improve trending’s ability to forecast longer-range growth and re-

development. These methods show very remarkable improvements in short and particularly

medium term growth forecast accuracy, but only modest increases in Representativeness and

scenario capability. Still, the best are clearly bargains: providng the highest ratio of

forecasting bang for the buck.

As examples, CARR-EL2 and INSITE (See Section 3) are both hybrid derivations of HRGF

trending programs, that compute long-term “horizon year” loads on a small area basis from

long-range land use projections (as for example from 30-year municipal land-use plans

prepared by a city’s planning department). Each also uses the land-use data in its pattern

recognition and hierarchy control, too. By test, either program shows a noticeable

improvement in one to two-year forecast accuracy over the best HRGF or other trending

methods, and a greater margin of improvement farther out – perhaps as much as a 60%

improvement in accuracy 15 years out. The improvement is quite noticeable (Figure 2-11)

for what proves to be a very modest increase (maybe 20%) in user effort.

Figure 2-11: Use of very limited land-use simulation concepts in a hybrid HRGF trending method

(thick solid line) makes a very noticeable improvement in its mid-range forecasting, as shown here

compared to the “trending vs. simulation” example shown in Figure 2-10.

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3. Summaries of Commercially Available, Credible Tools for Spatial

Electric Load Forecasting (In alphabetical order)

CARR-EL-2

CARR-EL (Carrington Electric Load) is a stand-alone program for spatial electric peak load

forecasting that has been available for over a decade from a series of small African and

European software firms, always associated with the program’s developer, Prof. John

Carrington. CARR-EL applies an HRGF method numerically and at a very high spatial

resolution to trend peak weather-adjusted electric demand.

CARR-EL was the first commercial program to use HRGF and lacks some of the subsequent

refinements made to that method. Many people suspect it was originally no more than a

commercialized, cleaned up form of INSITE (discussed later in this section). Regardless, it

was used for several studies in Morocco (Rabat, Casablanca, Tangier) in the late 1980s. After

some fine-tuning it was sold to several European utilities in the mid 1990s and applied to

many large urban areas including Athens and Rome at an UG vault or service transformer

resolution (small areas of about 500 kW peak demand each, defined by the service areas of

MV/LV transformers). In these and other applications, CARR-EL worked with what US

utilities would call TLM (Transformer Load Management) data on a small area basis. In the

one- to five-year-ahead time period the basic algorithm (non-hybrid) established new five-

year ahead accuracy levels in back to back tests against other methods, and proved easy to

use. There is a gas-system equivalent, CARR-NG, using the same forecast engine.

At present, the only version of this program in use is CARR-EL-2, a hybrid version of the

original HRPF that uses current and horizon-year land-use data to provide additional pattern

recognition/horizon year load data to the HRGF forecast algorithm. The land use feature can

be turned off, in which case CARR-EL2 is essentially a pure HRGF spatial trending method.

The method is similar to Quanta’s hybrid version of the INSITE shareware, except CARR-

EL-2 compares land use data for the present and horizon year and then works with the

difference of the two as its horizon target. (Quanta’s INSITE uses just horizon year land use.)

Application, whether pure trending or hybrid, is limited to 128,000 small areas, but this is

sufficient for very large systems since the average small area (a service transformer in a

European type system) typically has a load of around 250 to 500 kW.

CARR-EL in either form is straightforward to apply but data editing and program operation

are a bit clumsy. CARR-EL and CARR-EL2 were written in C++ and use an Excel front end

that is not integrated into the program: the user has to edit data in Excel, convert it to a file,

and input that file to CARR-EL. Making changes means re-using Excel and generating an

updated file, or changing the file itself with a text editor, then re-entering the data file and re-

running CARR-EL. This makes editing and calibrating forecasts a cumbersome process.



At one time CARR-EL was in use by as many as 35 utilities in Africa, the Mediterranean, and

southern Europe. But as of January 2007, Quanta could confirm that CARR-EL2 was in

active use at only five utilities comprising perhaps 6 million connected meters. Those users

report they continue to apply the program, but worry about on-going support. (For the past

few years John Carrington has been semi-retired, is not actively teaching, and support for

CARR-EL-2 has been weak at times).

CARR-EL-2 is not listed in the comparison given in section A-3, because it is not available in

the US and is apparently not highly supported at present.

ELF-2

ELF-2 is a stand-alone program that applies a classic Lowry-Garin land-use simulation

approach (see section 2.6.2) to spatial electric load forecasting. In fact ELF-2 is arguably the

classic program: it is a direct translation into Virtual Basic of FORTRAN program code for

the industry’s earliest spatial load forecast land-use simulation programs: ELUFANT,

developed at Houston Lighting and Power (HL&P) by Lee Willis in 1977, and LANDUSE, a

version of that program modified at the University of Texas to model two metro-areas at the

same time, for joint use by what, in the 1980s, were separate utilities serving Dallas and

Forth-Worth (Dallas Power and Light and Texas Electric Service Company, now merged into

TXU) (Willis, Gregg and Chambers; Fischer).

ELF-2 implements the original algorithm from the 1982 ELUFANT/LANDUSE program

code (a pure Lowry-Garin model) completely and unchanged, but uses modern VB features

for input, output, and display. It uses that Lowry-Garin urban land-use simulation model to

predict utility customer-class growth on a small area basis, then converts land-use to electric

load on a small area basis using customer class hourly load profiles based on MV-90 data.

ELF-2 is not sold to utilities but applied only in studies as a contracted service by JF

Associates LLC, a small consulting firm located in the Pacific Northwest operated by two

members of the original HL&P ELUFANT and LANDUSE planning teams, both retired from

a 25+-year careers with major software vendors. They offer spatial forecast services to

utilities only in a five-state area of the northwest US. To avoid the known shortcomings of

the Lowry approach – its inability to forecast redevelopment well – JF limits their application

to regions where re-development is unlikely to be a significant issue, providing forecast

services and annual updates only to utilities serving just small and mid-sized cities and rural

areas. (Redevelopment is generally not an issue in small cities where the commute to outlying

vacant areas is short). JF Associates produces an updated forecast annually using ELF-2 and

provides the utility with a forecast data file and a “viewer” to examine it, query data, and

produce reports and transfer files to planning programs as needed.



ELF-2 shows its first-generation nature; what are user-definable setup variables in other

simulation tools are fixed in it, including the number of customer classes (nine), spatial

resolution (two choices: square areas of either 25.6 or 71 acres –1/5th

or 1/3rd

mile across), and

region size and shape (only rectangular regions can be modeled, either 40 by 80 miles at 25.6

acres or 68 by 136 miles at 71 acre resolution). It was the first industry program to apply

“proximity and surround factors” in a simulation and the ELUFANT/ELF-2 framework for

working with their coefficients is not as efficient as in later programs: its data needs and set up

time are noticeably more than for more modern land-use simulation forecast programs.

Still, ELF-2 would be completely understandable to anyone who has used any subsequent

Lowry-based simulation program, from CEALUS through FORESITE, and to some extent to

those who have used SLEUTH or LoadSEER, which are land-use approaches but do not use a

Lowry model structure. Interestingly, this first-generation Lowry-Garin code did very well in

comparison to more modern interpretations of the same algorithm (ABB’s FORESITE) in

direct comparison tests (Section 4). Quanta determined that ELF-2’s slight accuracy

disadvantage there was almost entirely due to the fact that it forecast the test areas at a 71 acre

(1/3 mile) as compared to 2.5-acre and 10-acre (1/16th

and1/8th

mile) spatial resolution.

ELF-2 has been used to forecast all or parts of six utility systems in the northwestern US

within the last four years: areas comprising about half a million connected meters in total.

Utilities working with JF Associates report the company is supportive and particularly

forthcoming with quick forecast updates and advice, as needed. However, JF’s two principles

freely admit that their forecasting service is partly a “retirement-hobby” and partly a business,

and while they clearly take their commitments seriously, they also told Quanta they have all

the customers they want and will not take others at the present time.

JF Associates contributed the time and effort required to produce the test results of the

original ELUFANT/LANDUSE algorithm reported in section A-3, at no charge.

FORESITE

FORESITE, available from ABB Network Management, is by far the most proven spatial

electric load analysis and forecasting tool in the industry; it has been on the market and

evolved for 17+ years and has been used in several hundred studies around the world. One of

the authors of this report (Willis) was directly involved in the early stages of its creation and

has used it extensively, both when first developed and as recently as the summer of 2007,

consulting with two utilities on the use of their licensed copies of that program.

FORESITE is based on the Lowry-Garin algorithm and methodology, but builds on lessons

learned in earlier Westinghouse/ABB programs (SLF, SLF-2 and Loadsite). FORESITE

utilizes a Lowry-Garin land-use simulation to predict utility customer-class growth on a small



area basis, then converts land-use to electric load on a small area basis using customer class

hourly load profiles. Database format and user interface lack some features of newer

programs, but FORESITE’s GUI and database are flexible and straightforward to master, and

a vast body of previous experience exists on how to use it well and economically.

When set up and calibrated properly, FORESITE’s version of the Lowry-Garin forecast

algorithm is uncannily accurate at predicting long-term Greenfield growth trends in and

around metro areas of any size. However, ABB’s own web-based literature about spatial load

forecasting confirms the program is prone to the weakness many Lowry models often exhibit

– it is not as accurate at predicting Brownfield growth (ABB, 2006). Still, the program’s

algorithm has a good track record with many utilities around the world and the program code

is by far the most proven available at present.

With respect to re-development, Quanta Technology designed a GIS-based “re-development

pre-processor” program under contract to one of the FORESITE utilities it worked with in

2006-2007, that applies a “Haining approach” (see references) to compute the potential for

selected areas within a metropolis to re-develop. It then prepares data that can then be fed into

FORESITE to make that program predict that re-development growth pattern. This “patch”

has proved reasonably successful, but is not as satisfactory a solution to the issue as the use of

programs using algorithms that forecast both Greenfield and Brownfield re-development in a

balanced way. However, it noticeably mitigates this potential cause of forecasting error.8

FORESITE is a stand-alone program that runs on any of several types of computing

platforms. However, options vary and it was not clear form talking with current users which

are available now and which are not: utilities interested are advised to check with ABB. In

the opinion of several utility users Quanta Technology surveyed, its set up and data

preparation benefit greatly from direct access (on the same platform) to complete ESRI Arc-

Info or Arc-View workstation including the full Geo-Data and Map Algebra option. More

than half of the users surveyed mentioned that customer support is barely sufficient and often

lacks SME specificity, and that development and improvement of the program under

maintenance contracts seems to be lagging (User ratings varied more than for any other

program surveyed, all the way from “poor” to “excellent”). Quanta’s most recent data shows

FORESITE is in active use now at about a dozen utilities worldwide comprising a total of

around 8 million connected meters.

8 Quanta’s re-development patch for Lowry models like FORESITE, ELF-2, and others is a pre-

processor, a separate program run in ESRI Map Algebra prior to running the simulation forecast: thus,

Brownfield and Greenfield growth are not balanced in a simultaneous analysis. Section 4’s Table 3-2

gives the test results: use of the pre-processor “patch’s” data file cut FORESITE’s ten-year-ahead

Brownfield-area error from 11.3%% without it to 8.3% (a 27% reduction) as compared to brownfield

error rates of 7.9%, 8.1% and 7.5% for PowerGLF, SLEUTH and LoadSEER, respectively. Thus, this

pre-processor cuts Brownfield area forecast error significantly but the results fall short of the best that

can be done by simultaneous analysis.



INSITE

INSITE is the name given to a set of “shareware” developed by utilities/universities in South

America in the 1980s. Quanta can find no one who can definitively say where the software

was first developed or when it was first used, but has been told several times the original was

written in Dartmouth Basic for utilities in Argentina, Brazil and/or Chile sometime in the mid

1980s. Subsequently it and its user’s guide were shared, added to, improved, modified,

bastardized, re-labeled and translated into several other computer languages – until it had been

exchanged back and forth among many utilities as several distinctly different programs. Lee

Willis first encountered the program while teaching a forecasting seminar in Argentina in the

late 1980s, its name spelled EN-SITE, an acronym for Electric – Spatially Integrated Trending

of Exponentials. The name has also been spelled as N-SITE, INSIGHT, and INSITDE.

From its earliest form all versions apparently used a hierarchical recursive small area trending

method based on a cluster-based template matching algorithm that fits S-curves. According to

comments in the program code and user’s manual, the algorithm is based on a series of IEEE

transactions papers on small area trending methods published in the 1980s at Westinghouse

Advanced Systems Technology (Brooks and Northcote-Green; Menge; Powell; Willis and

Tram; Willis, Powell, and Tram; Willis and Northcote-Green 1982, 1983, 1984), including

one that gave the source code for a pattern recognition clustering sub-routine for template

matching in a hierarchical recursive program (Willis, Tram and Vismor, 1983) which is used

for the “curve fitting.”

This program definitely influenced Prof. John Carrington’s development of CARR-EL (he

mentioned that to Lee Willis at a CIRED conference in the mid 1990s). Many people suspect

that INSITE shareware may be part of that program, but Carrington has never confirmed or

denied speculation to that effect. However, while early versions of CARR-EL performed

nearly exactly the same functions as INSITE, CARR-EL has always minimized RMS error on

a small area basis when doing its small area curve fitting, whereas INSITE, at least in all its

original shareware versions, minimized the R0 error measure.

9 It is practically impossible to

minimize anything except RMS error with algebraic methods and relatively difficult minimize

RMS with template methods. Therefore, CARR-EL is probably not the same program code.

Regardless, Quanta Technology has provided software it refers to as “Quanta’s version of

INSITE” with no license restriction to utilities it works with on some spatial forecasting

projects. None of the code provided is part of the original shareware: Quanta has re-written

the basic algorithms in Microsoft Access VB, improving the algorithm in several ways

including making it hybrid: Quanta’s version of INSITE implements a full HRGF/hybrid

algorithm, although it can implement just a hierarchical recursive trending.

9 RMS error is the R

2 (Riemann, second order) error metric: the sum of the squares of the residuals in a

curve fit. The R1

metric is the sum of the absolute values (un-squared) or the residuals, and

minimizing R0 in a curve fit means fitting the curve to data in a way that minimizes the largest value of

the residuals.



Quanta intends these versions of INSITE only as an interim solution for a utility that perceives

delays in implementing a full GIS-based simulation method like several described in this

section. Quanta’s version of this approach is offered only as part of a larger project that

includes a roadmap and evolutionary plan for eventual deployment of a full commercial

software tool such as PowerGLF, FORESITE, or LoadSEER. INSITE is always heavily

tailored to a utility’s data and application needs and is set up so that its data formats facilitate

the later transition plan to the ultimate GIS-implemented program.

Quanta’s INSITE uses an enhanced HRGF algorithm that functions either as a pure trending

or a hybrid algorithm, using:

1) Semi-soidal functions (S-curves) applied hierarchically to small areas and groups

of small areas, minimizing the R2, R

1 or

R

0 error metrics, as is best in each case.

2) Pattern recognition of spatial interaction among small areas to determine the

hierarchical grouping sets and trending rules

3) Horizon-year land-use counts for re-development and long-term growth

horizons.

INSITE’s forecasting advantage compared to other HRGF algorithms (CARR-EL, SERDIS)

is in item two above – other HRGF methods use an arbitrary structure for relaxing resolution

as they compute trends hierarchically in their bottom-up assessment steps. INSITE uses

pattern recognition to vary the hierarchical structure dynamically, in order to maximize co-

variance of certain interaction statistics. It uses both numerical and rule-based expert-system

techniques to do so.

In utility applications to date Quanta’s version of INSITE has been applied in both equipment

area (feeder or sub-feeder areas) and square grid bases. In side-by-side tests, INSITE provides

forecasts matching the best available in the one to three year ahead timeframe. Its long-range

(5-15 year ahead) planning capability (representativeness) when applied in hybrid form is

better than that of trending methods, but substantially less than that of simulation methods.

The original INSITE/En-SITE/N-SITE is probably in use at several dozen utilities around the

world. Quanta’s version is in active use at five major utilities in the United States comprising

12 million connected meters. Since Quanta’s version of INSITE is not intended for long-

term ownership but only as a transition step, it is not supported as commercial-grade software.

LoadSEER

LoadSEER® is a spatial electric load and energy efficiency analysis and forecasting tool

available from Integral Analytics (IA), a DSM and load analysis software company

headquartered in Cincinnati. LoadSEER’s development was sponsored by Duke Energy,

PacifiCorp, Nashville Electric, and Northern Virginia Power. Quanta Technology helped

design and test LoadSEER’s spatial forecast algorithm under contract to Duke and IA. ESRI

provided expertise and geo-data base support and development. IA then added a version of its



proven DSM model (Load@Risk) and completed the commercial-grade software for the

program under a separate contract with Duke. Quanta’s Lee Willis is currently under long-

term retainer to Duke, PacifiCorp, and IA to support continued development and future

applications of LoadSEER and compatible planning, but has no exclusive or preferred

contractual commitment to it.

LoadSEER was designed from the outset to support both T&D and DSM planning and so

takes a different approach compared to other programs discussed here, beginning with its

program structure and layout: the temporal (8760 hour time element) aspect of the model is

much more prominent. Other programs discussed here are about a 90/10 split of spatial vs.

temporal features: with the vast majority of their focus on the spatial aspect of load growth.

By contrast LoadSEER is about 60/40: its spatial analysis and forecasting module is as

comprehensive as any reviewed here (it is currently the most accurate spatial forecast method

available, see Section 4), but its load-curve analysis and DSM forecasting/modeling features

are much more extensive than anything Quanta has seen elsewhere.

LoadSEER applies a spatial land-use growth simulation method unique to it, a combination of

a cellular automata (Miranda) and something a bit like the “simultaneous conflicting models”

approach used in USGS’s SLEUTH (Clark and Leonard). This algorithm encompasses both

urban modeling and economic-choice concepts in a “three-evaluation plane” transition score

vector computation.10

But unlike Miranda’s or Clark’s algorithms, LoadSEER applies this as

an “agent based” algorithm in which three conflicting models (agents), each trying to predict

what and how land-use may evolve, are applied simultaneously and resolved in a vector-space

representation with something close to a simulated annealing type of relaxation optimization.

The preceding may sound technically complicated, but in fact the algorithm’s concept is quite

easy to understand (Willis, Stevie, Osterhus, Skinner and Phillips, 2008). Just like Lowry

and most other urban planning approaches, LoadSEER models growth as ultimately driven by

employment centers (urban poles). Also like most urban models it also assesses “local

factors” like proximity-to-road in order to rate the growth potential of any particular small

area. However, while the Lowry approach applies those local factors as linear, circularly

symmetric functions, and Haining applies them as linear function of distance (essentially the

same effect), LoadSEER’s local factors vary spatially, directionally, and temporarily in a

context-sensitive manner and are not necessarily “round” or linear.11

One current user

(Duke) applies LoadSEER with those local factor weighting coefficients determined

10

A cellular automata is a mathematical construct that represents location “things” (small area in this case) as

capable of being only one of several states (residential, vacant, retail, etc., in this case) with rules based on

conditions measurable at that thing about if and how and how quickly and when it can change from one state to

another. Lowry models model small area growth in a manner somewhat similar to a cellular automata, their

chief limitation being they can only model transitions of land use from one state (vacant) to something else. A

complete cellular automata approach permits changse from any state (e.g., vacant or developed) to any other

state (developed differently) and thus is far better at forecasting re-development. See subsection 2.6.3. 11

Agent based algorithms apply several different cellular automata rules simultaneously (“this is the government

changing land use,” this is developers changing land-use” etc.) and view which ‘wins” locally as a function of

economic viability and some randomness.



automatically from a survey/statistical analysis of its new customers about why they located

where they did. This seems to give very good results and certainly increases credibility of the

resulting forecast model.

LoadSEER also applies a second, completely separate form of analysis in addition to that

land-use analysis, one based on an assessment of economic value. This estimates if developed

small areas as well as groups of small areas would “benefit the region” as a whole if they

made a transition from their present land use status to another. In so doing, LoadSEER

compares potentially redeveloping interior areas of a city in competition to vacant, cheaper-to-

develop areas on the outskirts of a city, using a “traffic load flow” and an economic

comparison model based on Haining’s redevelopment regression models (Haining).

LoadSEER also copies directly the most successful aspect of SLEUTH’s “Clark model.” It

uses SLEUTH’s logic for forecasting early growth in rural, not-yet-suburban areas outside of

metro areas, at which SLEUTH was far superior to all previous spatial forecast methods. In

October in 2007, LoadSEER was further modified with additional “borrowed” code, receiving

INSITE’s rule base in order to improve its short-range forecasting by reducing “fitting error”

(see Figure 2-10) to nearly zero, and also to time growth rates in re-development areas.12

The basic version of LoadSEER produces deterministic spatial forecasts: a single, most-

expected peak load forecast for each small area for each forecast year: the type of forecast

produced by all other programs discussed here. An optional risk-based version includes

Integral Analytics’ Load@Risk software for probabilistic customer load curve modeling, and

additional spatial analysis logic to produce probabilistic forecasts that support risk-based

T&D, DSM, and combined T&D and DSM planning. This is an interesting and potentially

powerful feature, but even Duke Energy and PacifiCorp, the two major utilities that along

with ESRI sponsored development of LoadSEER, admit it is experimental and only being

used on a single limited basis (DSM planning at both utilities) at the moment: risk-based T&D

planning tools that fully use such forecasts are now under development.

LoadSEER is a stand-alone program but requires a “full-house” ESRI Arc-View workstation

as its platform. It is computationally intense and works best when run on a computer with

very high-speed, server-type disk drives: on a standard high-end laptop LoadSEER requires

eleven hours to do a 20-year ahead forecast for an area like metropolitan Salt Lake City; but

on a powerful workstation, only 42 minutes.

LoadSEER offers several noteworthy features not available in other software. It mixes and

matches equipment-based and square-grid small area formats seamlessly and “on the fly” –

the user can enter data in either format and request analysis and reporting in either, or both, as

needed. It can “dump” its forecasts at high resolution into various GIS formats, as well as

onto the nodes/branches in the Synergy and Cyme brand of distribution engineering model

12

This logic reduces the spatial fitting error most land-use approaches have to current feeder loadings by half

and filters near-term (one to three year) forecasts into a smooth trend (see section 2’s discussion of simulation

methods).



databases, as both load (for load flow) and customer count (for reliability analysis) data. It

has features to automatically calibrate its activity center and local factor automata coefficients.

LoadSEER is a relatively new program (first commercial use, 2007) and not as proven as

others listed here. It is undergoing a rather steep development curve and users report

receiving and loading as many as three new versions in a week. They also report some

lingering quirks that require “work-arounds.” However, all seem satisfied, because in back-

to-back comparison tests to other forecast programs on both a metro and non-metro area, its

forecasts were unexcelled in every time frame: it is the most accurate spatial forecast method

currently available (see Section 4).

LoadSEER is in active use at seven utilities in the US comprising a total of about 8 million

connected meters. Furthermore, several utilities using INSITE as an interim solution have

committed to merge LoadSEER into their on-going GIS development once finished. User’s

with at least one forecast completed using LOADSEER uniformly rate it as “very good” or

“excellent” and IA’s support at an average of “satisfactory.”

MetrixLT

Metrix is a multi-variate time-series trending and auto-regressive forecasting/meter-data

toolkit available from Itron. About a half dozen electric utilities have used it for geographic

load forecasting (projecting future load by areas, if perhaps not small areas in the sense of the

other programs listed here) within a region. It does not implement spatial algorithms but does

support multivariate analysis and trending of demand, energy, and can model complex

interactions among areas in a way somewhat sensitive to spatial issues. The tool provides the

user with myriad data import, display, trend analysis, and forecasting model options for

projecting customer count, peak demand, energy, and load curve shapes and their weather

sensitivities into the future. It is well designed and documented and supported by a large,

experienced staff (which has other programs to support, also).

Metrix can do area-by-area forecasts only if the number of areas is modest (practically

speaking, no more than one hundred), but Quanta Technology and many other utilities have

found it very useful for assembling and studying overall “global” forecasts for a region.

Quanta has written a macro that applies it hierarchically in order to improve forecast accuracy

– what might be called an “almost” HRGF-like application, but again this only works on

limited sets of small areas. Software, license and training are relatively inexpensive.

MetrixLT is in use at five utilities for area forecasting, comprising areas of about two million

connected meters in total, but forecasts nothing smaller than substation area trends. Users rate

the program as “satisfactory,” but note that Itron’s support as “excellent.”



PowerGLF

PowerGLF is a spatial electric load forecasting tool that has been available for a number of

years from NETGroup Solutions, a consulting company/Siemens PTI partner in South Africa

that provides considerable support to Eskom and other utilities throughout Africa and the

Middle East. PowerGLF has gone through considerable evolution, including one version that

was purely GIS based and current versions that are not, as well as versions with and without

particular algorithms or features. Although it has not been used in North America, NETGroup

Solutions has indicated it would consider providing the software to US utilities. NETGroup

Solutions has an agreement with Quanta Technology to provide it support on spatial forecast

applications if and as it needs support in the US and Canada. One US utility, currently a user

of INSITE as an interim solution, intends to install PowerGLF and other PTI-sourced software

once its GIS platform is finished.

The current PowerGLF is an MS-excel-template extension “program” that is different from

the other tools listed here, in that it does not have a specific forecasting algorithm attached. It

is perhaps best described as a support and data retention environment for small area electric

load forecasting. This program provides analysis, display and “support utilities” that permit

planners to assemble data and build up their own forecast in a “manual” bottom up approach.

It can be set up to work in either an equipment-based or square grid small area format, or in a

“mixed” format (some small areas are equipment areas or arbitrarily defined/shaped areas,

others are squares). However, once defined, its small areas cannot be changed.

When used without a forecast algorithm, PowerGLF’s forecast is created “manually” by

planners, using tools within PowerGLF that permit transfer of future municipal land use maps

from GIS systems, manual entry of trends and land use and planner judgment on a small area

by small area basis as planners see fit, and convenient selection from sets of typical growth

trends for each small area, etc. In this way it is basically a specialized “electric utility”

version of generalized tools like ESRI’s Map Algebra feature. Preparing a forecast with this

manual mode requires more time and work than for any other program listed here, although it

is quite flexible in what it can model and in the detail with which the user can represent areas,

causes, and load curves. Performing a forecast in this manner is straightforward but very time-

consuming, and requires above-average knowledge and skills in spatial forecasting. There

would also be issues of defensibility, too: such a forecast could be called subjective.

For these reasons, most PowerGLF utility users have linked some small-area forecast engine,

be it an HRGF taken from CARR-EL, or a land-use simulation algorithm into it. A version

with an older but proven, non-Lowry land-use algorithm was used in the tests reported in

Section 4. When used with the CARR-EL HRGF algorithm as its forecast engine, PowerGLF

produces good forecasts in the one to three year ahead range, roughly equivalent to INSITE,

but does not equal the 10- year figures for it in Section 4.



When interfaced with either ESRI’s Arc-Info or GE’s SmallWorld GIS systems (PowerGLF

was originally designed to work hand in glove with either) PowerGLF produces some of the

most impressive maps and diagrams of load growth trends and planning needs that Quanta has

seen: NETGroup Solutions has shown innovation in several areas of data display and forecast

communication. This ability is PowerGLF’s most highly rated feature by users.

Quanta’s best estimate is that PowerGLF is in use or has been applied in contracted studies on

about 35 utility systems worldwide, comprising perhaps 25 million connected meters. User

utilities (all outside the US) rate the program and user support as an average of “satisfactory”

but with a good deal of variance.

PUCG/E

PUCG/E (Peripheral and Urban Congestion Growth – Energy) is a spatial energy

consumption forecasting tool developed in India and in use by several electric, gas, and public

utilities in Northern and Central India and Southeast Asia. Quanta Technology could obtain

very little information about it. The software vendor Quanta was told to contact did not

provide information much beyond that provided by one utility user whose forecast Quanta

reviewed and used in subsequent planning studies.

PUCG/E uses an urban model apparently based on the Lowry model (user documentation

references papers by Lowry, Garin, Gregg, et al, and Willis and Tram). However, its land use

model has been modified from the pure Lowry approach, in that employment can be modeled

as distributed within a residential-shop land-use class rather than always associated with urban

activity centers (urban poles) (Ramasamy). This makes it applicable to cities with large areas

of mixed residential/small shops-commercial self-employment that one finds in many parts of

the developing world.

PUCG/E uses a “congestion” model about which few details can be found beyond one

conference paper by Ramasamy that explains how it models increasing load density within

developed urban areas as a type of “growth” in area (a 40-acre small area might actually

“grow” to the equivalent of 100 acres, at a certain “cost” -- a type of re-development model.

A unique feature of PUCG/E among programs discussed here is that the program potentially

can model all stationary energy needs including electricity, gas and oil, propane, steam heat,

charcoal, and wood. It will map pollution from all these sources on a small area basis.13

13

At the request of one EDPC member one point was investigated further: PUCG/E does not try to

model how emissions spreads with the wind or how it affects population centers. Applications doing

that use separate weather and pollution dispersion models. PUCG/E analyzes only the original

locations of pollutants, their amounts, and the timing of their release on a diurnal cycle.



PUCG/E has been applied to truly monstrous urban areas such as Mumbai and Calcutta

(Ramasamy), as well as rural and agrarian areas of northern India. It is discussed here for

completeness sake. PUCG/E is not available in the United States nor has it been applied to

any studies in the western hemisphere, nor could it be included in the tests described in

Section 4.

SERDIS

SERDIS (service distribution) is a combination spatial forecasting and distribution capacity

planning tool developed and sold in Eastern Europe and the Middle East by Elektrovojvodina

Novi Exch, a cooperative R&D commercial initiative of the Czechoslovakian National

Electric Utility and local universities.

SERDIS is best described as a predictive transformer load management program. It was

developed for and is used by a dozen small Eastern European utilities to forecast future load

on a service transformer-by-service transformer basis. North American utility planners should

keep in mind that these are service transformers in a European style distribution system, in

which service transformers are typically 500-1000 kVA in size; when applied to US systems

the small-area entities forecast would probably be individual lateral circuits/URD loops rather

than service transformers.

SERDIS uses a spatial forecast algorithm owing only a little to previous work anywhere else.

Although the developers liken it to an HRGF method, and user documentation provided with

it references papers on hierarchical S-curve trending by Willis and Carrington as well as

forecasting work done at the University of Missouri in the 1980s by Turin Gonen, its

algorithm does not apply a pure HRGF method. Instead, it fits a spatial-temporal manifold (a

3-D function in time and location) to the small areas around a high-growth area using multiple

regression to small area load histories in space and by location, and then extrapolates that 3-D

function to forecast how load diffuses (from one small area to another) geographically after

growing along an S curve trend in each locality. Its makers claim this addresses the same

needs as HRGF’s hierarchical blocking, and forecast tests somewhat bear this out: it forecasts

load growing into new undeveloped areas (i.e., in small areas where there are no service

transformers) by creating new “faux transformers” with zero capacity in undeveloped areas,

and its accuracy by test is roughly equivalent to that of HRGF methods.

One advantage of SERDIS is that, once the forecast is complete, it will perform an

optimization to determine how to add capacity/new LV circuits throughout the modeled

region in order to eliminate all projected overloads while minimizing total cost. Quanta

tested this algorithm and it seems to do its job. But again, it is locked into a European style of

system design: it sizes standardized dual-loop “EDF-style” MV-LV transformer-cable sets

and transformer load assignments from its table-driven optimization algorithm.



SERDIS is in use at about a dozen eastern European utilities comprising about 1 million

connected meters in total and one small US system in which the senior planner, from the

Europe has imported the program. Only five of twelve users in a list provided by

Elektrovojvodina responded to an e-mail survey, giving an average rating of “very

satisfactory” to both the program and the support. For US users a major barrier to use is that

the program and documentation are only in Czech and no US representative is available.

SLEUTH-E

SLEUTH is a GIS-based spatial land-use growth simulation program developed by several

universities and the US Geologic Survey (USGS) to project how and where water runoff and

flooding will increase as cities grow and human society expands its use of land.14

The

program code (C++) is in the public domain and can be downloaded at no cost. With the right

compilers and auxiliary software Quanta was able to get it working on a standard type of

office computer under MS Windows.

SLEUTH uses a spatial land-use development approach often called “the Clark model,” that

applies three different but coordinated urban models (one vaguely like a Lowry model)

simultaneously on a small area basis, using a time- and context-varying rule base that it adapts

to determine what parcels of land develop, to what, by when. It uses a cellular automata (see

footnotes earlier in LoadSEER discussion) based on these three sets of local results to

determine what growth actually occurs.

SLEUTH forecasts Greenfield growth on the outskirts of urban areas nearly as well as any

Lowry-based model, but is superior to Lowry approaches at forecasting Brownfield

redevelopment within cities. It’s rural and agrarian area forecasting – the ability to project the

spotty, occasional and near random-appearing patterns of growth that gradually fill in along

country roads in rural areas well outside of cities – is superior to anything else Quanta had

seen.15

Within the venue of predicting metro-area growth and its effect on water runoff, SLEUTH has

proven quite accurate in detailed, objective “back-cast” tests and has seen use for that

throughout the US. Quanta Technology did a project with a SLEUTH application to spatial

electric forecasting for a utility in 2006. The tool’s most obvious shortcoming – that it has no

capability to translate its land-use forecasts into forecasts of small area electric load – was

14

Urban land use development like paved roads and parking lots, along with building roofs,

dramatically increases the rate of water runoff during rains, making areas that were not flood prone

when natural much more subject to flash floods during heavy rains. USGS is experimenting with

SLUETH as a way to improve planning for dams and flood water channels in the eastern US.

15

Except the current LoadSEER, which uses a rural-areas algorithm taken directly from SLEUTH, and

therefore equals its accuracy.



easily fixed using a standard end-use load curve model in Excel. The resulting tool, which

Quanta calls SLEUTH-E, is satisfactorily in use at one utility.

SLEUTH has a unique algorithmic feature of potential value to some utilities. It makes great

use of the slope of land in predicting development (or lack of it). Slope is clearly a key

element of analyzing water runoff, and the entire program code is organized to work

effectively with USGS data sets on land-height contour and slope. This is potentially a very

useful feature to copy (rather than use directly, see paragraph below) for utilities that cover

mountainous or other types of territory where slope of the land is a real issue in precluding or

biasing development.

As originally developed, SLEUTH does not distinguish land-use categories in ways that make

for satisfactory modeling of electric load: to the original SLEUTH the distinction between a

paved parking lot and a ten-story office building was not significant (both pour rainwater off

and into storm drain systems quickly): so it was developed around land-use categories that

distinguish water accumulation and run off well but not electric density well. As a result,

SLEUTH-E’s “out of the box” electric forecasting performance was poor.

In a project for one utility, Quanta set up SLEUTH with land-use classes more suited to be

able to distinguishing electric load density than those the program originally used. While this

improved its performance a good deal, a basic problem seems to be that SLEUTH does not

calculate all the factors and growth tables needed to forecast high land-use development

density well. Quanta’s conclusions are that even with extensive modification, SLEUTH will

not quite equal the metropolitan electric density distinction capability of land-use simulation

programs like FORESITE, LoadSEER, and PowerGLF; it cannot forecast development of

really dense peak load well. Thus, it is most useful for electric load forecasting only in areas

where there will be no high-rise developments, and where mountains and terrain (i.e., slope)

are a big factor in forecasting. The utility using SLEUTH_E serves a very large, mountainous

non-metro region, with only agrarian, rural and small towns, and finds SLEUTH-E suitable

for its needs. There is no commercial vendor and maintenance available (Quanta will not

provide software maintenance but so far the utility has been able to maintain the code itself).

SLEUTH’s biggest contribution to the power industry is without doubt the concepts inside the

Clark approach (the use of three models, combining and uses the results of several growth

models simultaneously, and its rural forecast method). Within the last two years ideas from

SLEUTH have been incorporated into INSITE, LoadSEER, and are being worked into

PowerGLF by its that program’s developers. SLEUTH-E is in active use at only one electric

utility, a very large, mostly rural utility in the Americas but outside the US, with slightly less

than one million connected meters.



4. Comparison of Forecasting Methods

Eight of the ten commercial programs discussed in Section 3 were compared in spatial

forecast “back-cast” tests in which data from 1996-1997 was used to forecast 2007 peak

demands. Tests were done on two study regions. The first, of roughly 19,500 square miles,

included a major metropolis and its suburbs as well as the rural areas farther out; a region of

about three million population with three “urban centers,” several zones of substantial

redevelopment within the metro areas, a good deal of greenfield suburban growth, and some

outlying rural, sparsely populated areas with spotty growth.

The second test region was larger geographically, at 43,800 square miles, but had a far smaller

population. There were no metropolitan areas, only one small city and several towns linked to

it by a single interstate highway, all with nothing beyond suburban load densities, and low-

density agrarian development along country roads spread out around the rest of the region.

However, this region had pockets of extremely high greenfield load growth along the

interstate corridor in the period 1997-2007, due to growth influences beyond its boundaries.

For both test regions, weather-adjusted data from 1996-1997 was used to forecast weather-

adjusted peak demands for 2007, which were compared to recorded, weather-adjusted 2007

peak demands to determine forecast error. “Scenario” type data such as annual system-wide

growth rate (needed by all programs), and data on new highways or major employment

centers that developed, etc. (needed by some simulation programs), was in all cases the actual

development that occurred in the 1997-2007 timeframe.16, 17

Common data was used wherever possible (i.e., one set of land use data was prepared and

used in all cases where programs required land-use, etc.), both to reduce cost of the tests and

to assure consistency of comparison. Estimates of effort and cost given for each program

include estimated cost for program, training, support, learning, data gathering/“scrubbing,”

and all internal resources needed to do one year’s forecast) and are based on what would be

required to prepare all the data and set-up needed for each individually, if each were used

alone.

16

This issue resulted in a good deal of discussion and some disagreement in the ESPC meeting that

set up rules for these tests. The point of taking this approach is that this is not a test of the planner’s

ability to decide how to lay out a “future scenario,” nor a test of “representativeness” of the programs

(see Section 2). These were tests of forecasting accuracy in all cases and thus it was decided to

evaluate these programs when given “the right future” to forecast. These tests, then, compare mostly

the ability of the programs to determine the correct spatial distribution of future growth, and are valid

in that regard.

17 After consideration, Quanta is not of the opinion that this decision gives simulation and hybrid

methods an “unfair” advantage as compared to trending methods, a possibility discussed at the EDPC

meeting in December 2006. While it is true that simulation and hybrid programs accept more “data

about the future” (major possible new employers, future highways that could be added) than trending

methods do (for them future data is limited to only the control total of system-wide load growth), this

difference is inherent in the methodologies being tested. One can argue that all three types of

programs were given, as part of this test, accurate “future data” in all categories they use.



Test procedure, “rules” and evaluation criteria were determined by the EDPC members in its

December 2006 meeting in Salt Lake City.

Only programs that could be specifically run for these tests were evaluated.

Forecasts done previously on these or other areas were not evaluated or used.

Error was calculated and reported as described later in this section.

Two programs were run in two versions each. INSITE was applied in both its

trending and hybrid trending/land-use formats. FORESITE was applied with and

without the use of an external “re-development patch,” written in Arc-Info,

developed to improve that program’s brownfield forecast ability. Results for

those two paired tests are reported in the tables given here.18

Vendors were not permitted to provide help or advice on running the programs or

setting up the forecasts, to submit information on their products, or lists of users

to be surveyed in Section 5.19

The one exception is the ELF-2 program. JF

Associates provided their time to set up the rather unusual (1970s) flat file

structure for the test, but did so to Quanta’s specifications and under observation

by Quanta and one of JF’s customers, and without access to or knowledge of “the

correct answers” (the actual load growth in each region).

Each spatial foreceast program was applied to the tests by an EDPC member who

has the program in use at his or her utility and volunteered to do the test work.

Thus, each program was applied by someone experienced in its application

Quanta created program code for and tested the two basic small-area trending

methods reported here for reference purposes, and observed all tests.

Quanta gathered the data and assembled the test results, and created the results information

base and user-survey data discussed here and in Section 5. Quanta would like to thank AEP,

ComEd, Duke Energy, Pacific Power, Nashville Electric, Midwest Energy, NOVEC, and

Rocky Mountain Power for the time and effort they contributed for these tests, and ESRI for

their support on data issues.

18

After to the original tests, ELF-2 was run with the same re-development patch data set at the request

of JF Associates, and PowerGLF was run with it, too, in order to see and how it, too, would react to

the use of that data. Results are reported separately later in this section.

19 Quanta realizes that it could be viewed as a “vendor” of services connected to these programs: Lee

Willis either wrote or helped develop the ELF-2, FORESITE, and LoadSEER programs. Quanta is a

non-exclusive US support resource for both SERDIS and PowerGLF, has worked with Itron with

regard to its MetrixLT program, and provides help to utilities working with SLEUTH-E and INSITE.

However, as discussed at the EDPC meeting, Quanta works with all of these programs and more, and

is neutral with respect to any particularly method. In its involvement throughout, Quanta strove to

maintain impartiality in application and interpretation of results.



Base Comparison Data on Programs

Tables 3-1 through 3-4 give basic information about each forecasting tool: what company

supplies the software, the number of years it has been on the market, number of utilities using

it, and similar overview data. In all cases this information was gathered from users and

discussion among EDPC members and may not match claims of the program vendors: for that

matter it may not be absolutely correct. However, it represents the best data that Quanta and

EDPC members could gather. Bold figures represent “best performance” in each category.

Forecast Error Results

Two forecasting statistics are reported, Uf and Us (see Section 2.5). Both are spatial error

metrics, measuring how badly a program mis-forecasts the locations of growth. Uf can be

thought of as the percent of load growth that is not forecast in the correct feeder service area

or in one of the feeder areas immediately around it (sharing a common boundary). A 10%

error measure means that load growth is, on average, mis-located about one and one half

“feeder area widths” away from its correct location, 10% of the time. The logic behind Uf is

that growth forecast into the correct feeder area, or into a feeder area immediately nearby,

leads to distribution planning that is either correct of not seriously in error (a load transfer can

correct the error), but load that is mis-located farther away will have a more serious impact on

planning. Us is the same measure but applied at the substation level.

Uf is measured three years out, Us ten years out – both periods being roughly twice the

duration of the typical lead times at those levels of system design and appropriate to measure

overall short-and long-term impact on planning at those levels. In many cases these two

metrics are about equal: forecasting at the feeder level three years ahead is roughly as difficult

as forecasting at the substation level ten years ahead. These error statistics were computed

using space-domain SFA functions calibrated to the power systems in the two areas (Willis,

1983).

Error is reported in Table 3-2 for three types of forecast situations. The areas within each test

region were identified as belonging to one of three categories:

Brownfield areas, where growth is occurring in existing developed areas,

Greenfield areas, where vacant land is developing into suburban or urban uses by

developed land is generally not redeveloping

Rural areas, as defined by Clark, that land “demonstrably beyond the influence of the

metropolitan growth pattern.” (Clark and Leonard)

Forecasting error for each of these three categories was aggregated and is reported as a sum in

percent, allowing comparison of how the programs perform in different types of utility

planning situations. Brownfield, Greenfield, and rural error levels were weighted at 50/40/10

respectively to determine overall “average” error level, as was agreed by vote of the EDPC as

being representative of the cross-section of its experience and planning needs.



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erv

ice

Fore

casts

DS

M

pote

ntial

Optional

risk-b

ased

Inte

rfaces w

ith

econom

etr

ic

models

Inte

rface t

o P

ow

er

Facto

ry

als

o p

lans

capacity a

dditio

ns

Lim

ita

tio

ns

an

d i

ss

ue

s

availa

ble

only

as a

fore

cast

serv

ice

Patc

h m

ust be

develo

ped

specific

ally

for

each a

pplic

ation

limited t

o a

bout

400 s

mall

are

as

User

guid

e,

pro

gra

m,

not

availa

ble

in

Englis

h

Not

accura

te f

or

ele

ctr

ic

applic

ations in b

ig

citie

s

Uti

lity

Ye

as

of

Us

e

(# o

f u

tili

tie

s x

ye

ars

of

Co

mm

erc

ial

Use

)

--

30

30

0+

21

12

21

10

80

2

Cu

rre

nt

Ac

tive

Uti

lity

Us

ers

-

-6

12

07

8≈1

81

31

La

rge

st

Fo

rec

as

t D

on

e t

o

Date

- i

n G

W p

ea

k-

-0

.85

22

12

14

2.2

15

0.9

3.2

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rge

st

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rec

as

t D

on

e t

o

Date

- i

n s

qu

are

mil

es

--

18

,00

02

8,0

00

28

,00

01

18

,00

0400 a

reas

(see t

ext)

21

5,0

00

20

,00

05

20

,00

0

Quanta

/ public

dom

ain

10 7 27

auto

matically

inte

rfaces

with b

uild

ing p

erm

it d

ata

from

Metr

oS

earc

h**

*

square

grid,

1/1

6th

mile

Variable

hourly load c

urv

es

50

,00

0

Ta

ble

3-1

: B

asi

c O

ver

vie

w I

nfo

rma

tio

n o

n S

pa

tia

l L

oa

d F

ore

cast

Met

ho

ds

Tes

ted

Be

st

fore

ca

stin

g

in m

ou

nta

ino

us

are

as,

etc

.

3-D

fu

nctio

n o

f

sp

ace

an

d t

ime

like

an

HR

GF



Fo

rec

as

tin

g T

oo

lC

las

sic

Tre

nd

ing

Go

mp

ert

z

Tre

nd

ing

EF

L-2

FO

RE

SIT

Ew

. re

dev

patc

hIN

SIT

Ehyb

rid v

ers

ion

Lo

ad

SE

ER

Me

triX

LT

Po

we

rGL

FS

ER

DIS

SL

EU

TH

-E

Th

ree

- y

ea

r a

he

ad

ac

cu

rac

y ,

U- f

me

tric

* a

ve

rag

e o

ve

r a

ll

are

as

28

.7%

20

.6%

8.4

%8

.4%

7.3

%1

0.4

%7

.4%

6.4

%14.8

%8

.0%

10

.9%

7.4

%

in o

nly

bro

wn

fie

ld

me

tro

are

as

25

.0%

16

.8%

8.8

%1

0.0

%7

.8%

9.0

%6

.6%

6.6

%13.2

0%

7.2

%1

2.0

%7

.4%

in o

nly

gre

en

fie

ld

su

bu

rba

n a

rea

s3

5.0

%2

5.8

%7

.0%

6.0

%6

.1%

11

.0%

7.8

%6

.0%

17.7

0%

8.8

%8

.0%

7.6

%

in o

nly

ru

ral

are

as

25

.0%

21

.3%

11

.4%

8.8

%8

.8%

15

.0%

9.5

%6

.5%

13.0

0%

8.8

%1

5.0

%6

.5%

Te

n -

ye

ar

ah

ea

d

ac

cu

rac

y ,

U-s

me

tric

**

ave

rag

e o

ve

r a

ll a

rea

s

31

.7%

22

.9%

10

.0%

9.5

%8

.2%

12

.2%

9.8

%7

.5%

15

.9%

8.5

%1

3.1

%8

.1%

bro

wn

fie

ld m

etr

o2

7.0

%1

8.1

%1

1.0

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%8

.3%

11

.6%

8.8

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.2%

13

.7%

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%1

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.0%

gre

en

fie

ld m

etr

o3

8.0

%2

8.0

%8

.5%

7.0

%7

.2%

12

.8%

10

.5%

8.0

%1

8.4

%9

.1%

11

.9%

8.7

%

rura

l a

rea

s3

3.0

%2

8.1

%1

0.0

%9

.1%

9.1

%1

3.2

%1

2.0

%7

.1%

17

.9%

9.7

%1

9.0

%7

.0%

Rep

res

en

tati

ve

ne

ss

-

sc

en

ari

o c

ap

ab

ilit

y***

2

3

6

7

8

4

7

10

6

9

3

9

****

Me

tro

Se

rach

is a

co

mm

erc

ial p

rovi

de

r o

f d

ata

ba

ses

of

bu

ildin

g p

erm

it a

nd

sta

rt c

ou

nts

on

a s

pa

tial b

asi

s .

***

Rep

rese

nta

tive

ne

ss r

atin

g a

ga

inst

sce

na

rio

list

pre

pa

red

by

the

ED

PC

me

mb

ers

, se

e t

est

. 0

to

10

sca

le,

with

be

ing

be

st.

* U

-F e

rro

r m

ea

sure

is p

erc

en

t o

f fe

ed

er

loa

d g

row

th t

ha

t is

mis

-lo

cate

d s

pa

tially

at

lea

st "

on

e f

ull

fee

de

r a

wa

y" f

rom

its

corr

ect

fe

ed

er

(se

e t

ext

)

**

U-S

err

or

me

asu

re is

pe

rce

nt

of

sub

sta

tion

loa

d g

row

th t

ha

t is

mis

-lo

cate

d s

pa

tially

at

lea

st "

on

e s

ub

sta

tion

aw

ay"

fro

m it

s co

rre

ct s

ub

sta

tion

s (

see

te

xt)

Ta

ble

3-2

: F

ore

cast

Acc

ura

cy V

alu

es D

eter

min

ed f

or

Sp

ati

al

Lo

ad

Fo

reca

st M

eth

od

s

Tes

ted



Fo

rec

as

tin

g T

oo

lC

las

sic

Tre

nd

ing

Go

mp

ert

z

Tre

nd

ing

EF

L-2

FO

RE

SIT

Ew

. re

dev p

atc

hIN

SIT

Ehyb

rid

vers

ion

Lo

ad

SE

ER

Me

triX

LT

Po

we

rGL

FS

ER

DIS

SL

EU

TH

-E

Es

tim

ate

d l

ab

or

ho

urs

to

ap

ply

an

nu

all

y,

pe

r 1

M

cu

sto

me

rs

120

120

15

00

72

58

45

12

02

00

45

02

00

20

00

12

08

00

Typ

ica

l c

os

t fo

r s

oft

wa

re a

nd

firs

t c

om

ple

te u

se

, a

ll c

os

ts,

pe

r 1

M c

us

tom

ers

$25K

$25K

$9

0K

pe

r stu

dy

$2

00

K?

Δ=

$5

0K

+$

25

0K

$2

0K

$1

50

K$

75

k$

15

0K

Ea

se

of

us

e r

ati

ng

on

0

- 1

0

(be

st=

10

) s

ca

le,

ba

se

d o

n X

cu

sto

me

rs f

ee

db

ac

k

--

Ra

tin

g =

7

fro

m 4

utilit

ies

Ra

tin

g =

8

fro

m 7

utilit

ies

Ra

tin

g =

9

fr

om

5 u

tilit

ies

Ra

tin

g =

5

fro

m 8

utilit

ies

(no

ne

US

)

Ra

tin

g =

8

fro

m 3

utilit

ies

(1 in

US

)

Ra

tin

g =

5

fro

m t

he

sin

gle

use

r

Me

ets

cu

rre

nt

an

d f

ore

se

ea

ble

pla

nn

ing

ne

ed

s

0-1

0 (

10

=b

es

t)

--

Ra

tin

g =

9

fro

m 4

utilit

ies

Ra

tin

g =

9

fr

om

7 u

tilit

ies

Ra

tin

g =

4

fro

m 5

utilit

ies

Ra

tin

g =

5

fro

m 8

utilit

ies

(no

ne

US

)

Ra

tin

g =

7

fro

m 3

utilit

ies (

1

in U

S)

Ra

tin

g =

8

fro

m t

he

sin

gle

use

r

Cu

sto

me

r s

erv

ice

ra

tin

g o

n

0 -

10

(b

es

t=1

0)

sc

ale

, b

as

ed

on

X c

us

tom

ers

fe

ed

ba

ck

--

Ra

tin

g =

9

fro

m 4

utilit

ies

Ra

tin

g =

8

fro

m 7

utilit

ies

Ra

tin

g =

9

fr

om

5 u

tilit

ies

Ra

tin

g =

8

fro

m 8

utilit

ies

(no

ne

in

US

)

Ra

tin

g =

7

fro

m 3

utilit

ies (

1

in U

S)

not

surv

eyed:

pro

gra

m

is n

ot

support

ed

under

license (

see

text)

Ra

tin

g =

6

fro

m 6

utilit

ies

Ra

tin

g =

4

fro

m 6

utilit

ies

Ra

tin

g =

6

fro

m 6

utilit

ies

Ra

tin

g =

8.5

fro

m 4

utilit

ies

no

t su

rve

ye

d:

pro

gra

m

is n

ot

su

pp

ort

ed

un

de

r lic

en

se

(se

e t

ext)

no

t su

rve

ye

d:

pro

gra

m

is n

ot

me

an

t a

s a

lo

ng

-

term

so

lutio

n (

se

e t

ext)

$4

5K

Ta

ble

3-3

: U

sag

e C

ost

an

d O

ther

Da

ta E

stim

ate

d f

or

Sp

ati

al

Lo

ad

Fo

reca

st M

eth

od

s

Tes

ted



The “Representativeness-scenario capability” score shown in Table 3-2 is the number of

scenario features listed in Table 3-4 that the project team believed the program could

accommodate well: meaning there is a way within the program’s structure and data

format to represent the change in future conditions and that it has the analytical

wherewithal to model the impacts of the change well. The ten items listed in Table 3-4

were selected by the EDPC members by e-mail voting consensus in early 2007.

Sce

nar

ioC

lass

ic

Tre

nd

ing

Go

mp

ertz

Tre

nd

ing

EF

L-2

FO

RE

SIT

Ew

. red

ev

pat

chIN

SIT

Eh

ybri

d

vers

ion

Lo

adS

EE

RM

etri

XL

TP

ow

erG

LF

SE

RD

ISS

LE

UT

H-E

Cha

nge

in o

vera

ll cu

stom

er

grow

th r

ate

for

one

year

11

11

11

11

11

11

Cha

nge

in o

vera

ll cu

stom

er

grow

th r

ate

for

all y

ears

11

11

11

11

11

11

Cha

nge

in p

er-c

apita

con

sum

p-

tion,

not

cus

tom

er g

row

th

11

11

11

11

11

1

New

maj

or e

mpl

oyer

add

ed in

year

X1

11

11

11

1

Maj

or n

ew h

ighw

ay a

dded

in

year

X1

11

11

11

11

Pla

nned

re-

deve

lopm

ent n

ear

dow

ntow

n –

mig

ht h

appe

n1

11

11

11

Pla

nned

re-

deve

lopm

ent n

ear

dow

ntow

n –

will

hap

pen

11

11

Loss

of m

ajor

em

ploy

er in

yea

r X

11

11

11

Maj

or r

etire

men

t bas

e gr

owth

in

resi

dent

ial s

ecto

r1

11

Ele

ctric

veh

icle

s –

anal

yze

spat

ial c

omm

utin

g lo

ads

11

Sco

re2

36

78

47

106

93

9

Ta

ble

3-4

: D

eta

ils

of

Rep

rese

nta

tiv

enes

s S

core



Additional Tests

The re-development pre-processor data file used with “FORESITE with re-devel patch” was

applied to the ELF-2 and PowerGLF program in tests not listed in the tables. It was first

translated from the 11 land-use class, 2 ½ acre format originally created for FORESITE to a

9-land use class 71-acre format for ELF-2 and 12-class, 40 acre format for PowerGLF, using

ESRI’s Arc-view Geo-data software. The resulting data sets were then used with ELF-2 and

PowerGLF in the same manner as applied to FORESITE. ELF-2’s Lowry-Garin algorithm

responding roughly as did FORESITE: ten-year aheadBrownfield error dropped from 11% to

8.8% (a 25% reduction) leading to an overall weighted error rating of 8.8%. by contrast,

PowerGLF’s brownfield error when using this patch dataset dropped from 7.8% to 7.75%

(<1%), with its weighted error rating essentially unchanged.

Further Comparison

Figure 3-1 compares Uf for the tested methods as a function of forecast period. Error

increases exponentially with forecast period for all methods, but simulation methods do

relatively better in that regard. Results for substations were similar over a longer period, but

provided no further information on which method has accuracy advantages over the others,

and so are not plotted.

Figure -3-1. Comparison of the twelve test results versus forecast period in years.

0 1 2 3 4 5 6 7 8 9 10

Years Ahead

35%

30%

25%

20%

15%

10%

5%

0%

Err

or

in F

ore

cast

ing

Sw

itch

able

Fee

der

Nei

gh

bo

rho

od

Lo

ads-

%

0 1 2 3 4 5 6 7 8 9 10

Years Ahead

35%

30%

25%

20%

15%

10%

5%

0%

Err

or

in F

ore

cast

ing

Sw

itch

able

Fee

der

Nei

gh

bo

rho

od

Lo

ads-

%

INS

ITE

(tr

end

ing

)

SE

RD

IS

FO

RE

SIT

E

3rdO

rder

Po

lyn

om

ial

Cu

rve

Fit

Go

mp

ertz

Cu

rve

Fit

INS

ITE

hyb

rid

EL

F-2

Lo

adS

EE

R

FO

RE

SIT

E w

pat

ch

SL

EU

TH

-E

Po

wer

GL

F

Met

rixL

T

INS

ITE

(tr

end

ing

)

SE

RD

IS

FO

RE

SIT

E

3rdO

rder

Po

lyn

om

ial

Cu

rve

Fit

Go

mp

ertz

Cu

rve

Fit

INS

ITE

hyb

rid

EL

F-2

Lo

adS

EE

R

FO

RE

SIT

E w

pat

ch

SL

EU

TH

-E

Po

wer

GL

F

Met

rixL

T

SE

RD

IS

FO

RE

SIT

E

3rdO

rder

Po

lyn

om

ial

Cu

rve

Fit

Go

mp

ertz

Cu

rve

Fit

INS

ITE

hyb

rid

EL

F-2

Lo

adS

EE

R

FO

RE

SIT

E w

pat

ch

SL

EU

TH

-E

Po

wer

GL

F

Met

rixL

T



Figure 3-2 compares forecast accuracy and operating cost, and is based on three sources. The

first is data from tests done in 1982-1984 and reported in a peer-reviewed IEEE paper (Willis

and Northcote-Green, 1984). The second is a set of tests done in the same way on an

additional five forecast methods and reported in the book Spatial Electric Load Forecasting-

Second Edition. Together these two sources compare 19 forecast methods on the basis of

overall accuracy and cost of operation (The diagram used as the basis for Figure 3-2 is from

that book, which gives details of the 19 programs referred to there). Finally, the third source

is the test data here (Tables 3-1 through 3-2).

Figure 3-2: Diagram comparing nineteen spatial forecast algorithms on a common basis of accuracy

and overall cost of use, from Spatial Electric Load Forecasting – Second Edition, showing estimated

position of the spatial forecast methods listed in Tables 3-1 through 3-3 and plotted in Figure 3-1.

The ELF-2 program (former ELUFANT) contains the only algorithm in these most recent tests also

among the original nineteen methods summarized in the book. Both the error measure (vertical scale)

and cost basis (horizontal scale, are slightly different than the error metrics and cost estimates used in

Tables 3-1 through 3-3 and have been adjusted to be consistent.

16

0 .20 .40 .60 .80 1.0

Cost of Application - (Relative Cost of Application -

1

2

3

4

5

4

5

6 7

8

9

10

11

12 13

14

15 17

19

ELF - 2 is exactly method 12

FORESITE

LoadSEER

.

SERDIS

MetrixLT

SLEUTH - E

INSITE - trending

Curve fit trending

PowerGLF .

1.0 .80 .60 .40 .20 0

)

INSITE - hybrid

U

f -

Re

lati

ve



5. Survey of Users

This section reports on a survey of planners at utilities currently doing spatial load forecasting,

about their methods and the tools they use. An internet survey form was used with follow-up

phone conversations in some cases. A total of 61 utility planners were surveyed, some outside

North America. One should keep in mind that all were from utilities that already do spatial

forecasting and thus this is not a representative cross-section of industry-wide experience.

The project team encountered several utilities that stated they are thinking about taking spatial

forecasting up in the near future, but their comments are not included.

Average respondent:

1.38 technical or MBA degrees 19 years of utility experience

13 years in T&D planning 63% are member of IEEE or IEE

82% have attended at least one external spatial forecasting seminar

35% have bosses who have done spatial forecasting in the past

82% have a copy of first or second edition of SELF

89% have copy of first or second edition PDPRB

23% have attended IEEE PES, IEE PC, or CIRED meeting in last 5 years

Q1: How long has your utility been doing spatial load forecasting?

Question 1 to 3 3 to 6 6 to 9 10 or more Don't know Total

Number of Years Your Utility

Has Done Spatial Load

Forecasting

13 11 7 8 22 61

Most utilities who replied “don’t know” had a respondent with less than five years in

Planning.

Q2: Have you used the same basic method (even if the software has evolved), all this

time? And follow ups.

Question Yes No Don't know Total

Have you used the same

basic method, even if

software, etc. evolves

slightly, all this time?

8 32 21 61

Question 1 to 3 3 to 6 6 to 9 10 or more Don't know Total

How Many Years Has Your

Company Used Your Current

Method?

22 7 4 5 23 61

QuestionImproved

Forecasting

Better service,

support

Lower cost to

use

Came with

Enterprise IT

system

Don't know Total

Why Did Your Company

Adopt Your Current Method?18 13 4 3 23 61



Q3: How far ahead does your company forecast?

Follow-up: how far ahead do you plan various levels of your system?

Here, the survey looks at how far ahead forecasts are done and plans are made. Fewer than

half the utilities surveyed use spatial forecasts for high-voltage grid planning (all those that

did not are in the US, many in situations where de-regulatory rules prohibit distribution and

grid planners from communicating). Roughly 20% of planners surveyed are not responsible

for substation siting and planning. Detailed examination of this data shows it is slightly

inconsistent – one or two utilities plan one or more levels of their system farther out than they

say they forecast (possible but unlikely). This was not followed up.

Question 1 to 3 3 to 6 6 to 10 10 to 15 16-25 Over 25 Total Average Yrs

Planning Horizon: Farthest

Year Out Planned9 17 15 10 8 2 61 9.2

13 17 20 43 104

Level of System 1 to 3 3 to 6 6 to 10 10 to 15 16-25 Over 25 Total Average Yrs

Transmission (regional grid) 3 7 11 3 1 25 11.9

Primary (HV/MV) substations

and HV lines feeding them6 24 15 6 1 52 6.5

Primary (MV) distribution

lines 35 18 6 2 61 3.7

Service (LV) level 58 58 1.3

Figure 5-1. Plot of planning period versus level of system planned.

0 5 10 15 20 25 30

Years Ahead

Rela

tive N

um

ber

0 5 10 15 20 25 30

35

Forecasting Period

Grid (EHV)

Substations (HV/MV

Feeders (MV)

Service (LV)



Q5: How often do you update your spatial forecast?

Follow-up: If not annually, do you have a separate short-term forecast method, or a

way of updating forecasts for year-ahead planning, done annually?

Question Every year 2-3 Years Less Often Total

How Often Do You Update

Your Spatial Load Forecast28 23 10 61

Yes 3 19 10 32

No 25 4 - 29

Do You Have a Separate

Process for Short-Range (Year

ahead) Forecasts?

Q6: What is the value added by Spatial Load Forecasting?

Each respondent was asked to allocate 10 points among the categories below. We have

normalized the results to percent. Interestingly, improved credibility (sum or both internal

and external) is the single largest value seen, at 26% of value.

Question

Improved

NPV of

Plans

Obtain

Sites &

ROW better

Improved

Planning

Focus

Better

ability to

coordinate

with other

plans

Improved

Internal

Credibility

& Defense

Improved

Internal

Credibility

& Defense

Other Total

Where is the Value Seen

From Spatial Forecasting20% 21% 18% 12% 11% 15% 2% 100%

Q7: What problems have you had with spatial forecasting?

Again, respondents had 10 points to allocate, and again, results are normalized to percent.

Not surprisingly, data and its consistency is over a third of all problems reported.

QuestionData and data

consistency

Keeping

planners

once they are

trained

Limited

resources or

too little time

Method

doesn't meet

all our needs

Vendor

support or

software

problems

Other Total

What problems have you had

with Spatial Forecasting?35% 19% 16% 11% 15% 4% 100%

External



Q8: Survey of Forecast Tools

Here, utility users were surveyed on their satisfaction with the forecast method they have now. Scores given to categories are: Poor = 1, Satisfactory =2, Good = 3, Very good = 4, Excellent = 5

Current tool meets

our needsEFL-2 FORESITE

w. redev

patchINSITE hybrid version LoadSEER MetriXLT PowerGLF SERDIS SLEUTH-E

Poor

Satisfactory 1

Good 2 3 6

Very good 3 3 2 6 5 1

Excellent 1 2 1 4

Average 3.8 4.4 3.7 3.8 4.0 4.0

Vendor's software

service & supportEFL-2 FORESITE

w. redev


Poor

Satisfactory 1 1 1

Good 2 8 4

Very good 4 2 2 4 1

Excellent 2 4 4

Average 4.3 3.2 4.7 3.6 3.2 2.0

Vendor's

Application

Support

EFL-2 FORESITEw. redev


Poor 2 1

Satisfactory 2 1

Good 2 1 2 8 3

Very good 3 2 5 2

Excellent 1 2 3

Average 3.8 1.8 4.0 3.6 3.4 1.0

Application

support obtained

from other than

vendor



Poor

Satisfactory

Good 1 1 2

Very good 2 1 3 3

Excellent 2 1

Average - 4.2 3.5 3.6 - 5.0

Overall satisfaction

with current

method



Poor

Satisfactory 2 4 2

Good 2 2 5 2

Very good 3 3 2 6 1 1

Excellent 1 2 2

Average 3.8 4.4 3.0 3.4 2.8 4.01.9 2.5 3.2

2 2 3

2 2

Small Area Trending (usually

devellped in-house)

3 2

3.4 4.4 4.1

2 3 4

2 2

3 1


devellped in-house)

1.7 1.9 4.2

3

2

3 3

1 2 1


devellped in-house)

3 3

2 2 1

1.9 2.3 4.2

1 3

2

2 3

2 2 1


devellped in-house)

3 2

1.9 4.02.6

2


devellped in-house)

3

2

2 2

2

2

2

1

1

2

1

3

3.3 2.7

1

4

4.8



Comments submitted with survey

They [Itron] have some brilliant mathematicians in their support group in Oakland, CA.

We developed our own forecast method and it does what we want. Management thinks we should have a

package used by many other utilities, but so far hasn’t wanted to spend the money.

We have not used our FORESITE tool in several years but will need to in the next year or so. I expect they

have an improved version now that requires less data and is easier to use.

We rated INSITE in the survey as only marginally good because it isn’t a long-term solution for us. But it

is a good method for us until we can get what we need from [our GIS-IT department]. I can’t imagine any

better non-GIS method.

Our credibility with management went way up when our federal (Canadian) hydrology department adopted

SLEUTH for several rain-flood distribution studies.

[SERDIS] is a very wholesome (we think this eastern European utility planner meant complete) package for

tactical planning. For strategic far-range planning it is not so good.

I think we will use [our internally developed package] for short-range and hire long-range forecasts done

every few years. I don’t see how we can justify training people to do those types of studies.

Frankly, it makes sense to let [JF Associates] do the forecasts. It doesn’t matter that the algorithm [ELF-2]

is old, it seems to work well and they don’t charge even half as much as anyone else would. We got

pushback from [our VP] about using a small unknown company, but the price was so much lower, and he

changed his mind when we told him Lee Willis originally wrote the algorithm.

Management puts more demand for long-range planning than we can deliver right now. We are trying to

hire two new experienced planners. We’ll do more long-range forecasts then.

The only reason we get what we need from FORESITE is that we use [ESRI’s] Map Algebra to adjust data

in and forecasts out.

We’ll get by with what we have for a year to two and then get LoadSEER in ’09 or ’10. I like what they

showed us, but we are not going to help sort it out, even for the beta-user’s discount.

Itron is the only software vendor I’ve seen that seems to understand how to support application software.

They charge a good deal, but they provide value for the money.

The test results done on our system last year proved that it works, but I wish we had someone internally

who really understand what an expert system is and how it [INSITE] works. Some of our executives think

non-numerical methods are just “voo-doo math.”

PowerGLF is not difficult to use but requires a lot of tedious data entry and checking, and a person really

has to be expert at using its many data balancing and adjustment features to make it to work well. It is a

difficult program to learn because one needs to use so many different features simultaneously to assemble a

good forecast.



I agree that the term “grade logic” gets a lot less push-back than “fuzzy logic,” but I wish there were some

other way to describe the algorithm than “hybrid” – several non-technical types in upper management think

we are analyzing hybrid electric powered cars. (referring to Q’s INSITE version).

I have a group of senior planners who wrote the current trending method before I became manager and

believe it is very good. They get defensive when we discuss changing to a commercial program, although

they agree it does not meet all our needs.

We spend a good deal of our forecast labor manipulating data and output maps with [ESRI’s] Geo-Data,

rather than with the FORESITE program itself.

[The two JF Associates] seem more interested in seeing that we get what we need than in making money on

every service. They each made a trip over here without charging to help us. I doubt they’ll want to stay in

business for too many more years, but until they shut down we’ll be their customer.

We’re supposed to seek help from our local [NETGroup] office, but the Elardaupark office has the only real

expertise [with Power GLF], the others can do little more than take a message and pass in on.

The secret of good T&D forecasting has nothing to do with algorithms and forecast accuracy. It boils down

to producing pretty maps that communicate to community groups and regulators why the city will grow and

the reason load with grow with it and why we need substation sites.

FORESITE works okay, but the data takes forever to develop and it doesn’t forecast anything in the inner

1/3rd

of our metro area, where we know we’re going to see a lot of slow, steady evolution of old

neighborhoods.

We would not have adopted LoadSEER if the guys in [our other operating company] did not already have it

running. It looks too complicated. But once you get it up and running it is easy to use if you turn all the

risk-calculations off, and it has not crashed once.

SERDIS works only because I used it [in Europe for many years]. But with the dollar changing value the

annual user fees are rising and I’m getting pushback from management. I wish Itron would add something

like this to their DAA.

MetrixLT has hundreds of options and support features to learn, some that we can’t even find in advanced

math books. We’re not tapping even 10% of its capability.

Based on planning results it is probably impossible to justify ever replacing INSITE, but management wants

the credibility that comes from using land use methods and good display maps for defense of substation site

requests, etc. We’ll get [PowerGLF] as a front and back end but want to stay with the algorithm we use

now.

Itron doesn’t have to charge you much for the software because they nickel and dime you with services

required to get it to do what you want it to.

We don’t do a lot of thinking around here. [explanation for why they use an old trending program].

We can’t justify planning more than about 7 years ahead. Hard to see why a land-use method is needed.



NSITE did wake up everyone around here. It uses exactly the same database as our [old internal method],

was linked into our APS [internal on-line area planning system] just like the old method but is actually less

work to use, yet it cut forecast error three years ahead in half.

The old days when we didn’t spend any money were a lot less chaotic and hectic for us than now. We had

a lot of trouble getting back into long-range forecasting. I think we would have done better to start with a

new method: we only thought we remembered how to use [FORESITE] and it would have been easier if we

had picked something new that came with some training to help us.



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Spatial Electric Load Forecasting Methods for Electric Utilities

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