Northern Virginia Electric Cooperative (NOVEC)€¦ · • Extremely High volume customers (aka...

Northern Virginia Electric Cooperative

(NOVEC)

Customer Categorization

OR699/SYST699 Masters Program Final Project

Fall 2014

Prepared for Dr. Hoffman

Prepared by:

Abdulrahman Alsabti

Kevin Mitchell

Daniel Package

Executive Summary

Northern Virginia Electric Cooperative (NOVEC) is a power reseller headquartered in Manassas,

Virginia. As a not-for-profit organization, one of their primary goals is to reduce cost in order to pass the

savings along to the customer. One path for reducing cost is to minimize the predicted amount of power

purchased from wholesale electric grid providers. Accurate forecasting allows NOVEC to negotiate a

bulk price with wholesalers. If consumption is under-predicted, NOVEC must purchase additional power

on short notice to meet demand, often at a markup. If consumption is over-predicted, some portion of

electrical power purchased goes unused, the cost of which must be shared by the customers of the

cooperative. NOVEC’s current model for predicting forecasted power consumption involves a

combination of economic and weather forecasting combined with historical data from customers’ past

usage and the breakdown of residential versus commercial customers. NOVEC approached the George

Mason Systems Engineering and Operations Research department with the task of further refining the

categorization of customers to help feed their prediction model. The hope was that a more granular

definition of customer power consumption types (beyond just residential and commercial) would provide

more accurate inputs and allow refinement of the forecasting model. To that end, NOVEC provided the

project team with hourly power consumption history over roughly 1300 days for 1,150 customers for

which they had installed high end survey meters.

In order to define a quantifiable set of metrics to categorize customers, the project team worked with

NOVEC personnel to define a baseline for how they currently track power consumption and used their

internally defined metrics to develop a heuristic that could automate the process. Prior to this project,

customers were assigned a Billing Rate Code (BRC) based on the type of customer (school, large-power

business, small commercial, etc) and customers would be periodically analyzed based on two metrics:

Load Factor and Load Shape. Load Factor is the ratio for an arbitrary period of average power

consumption for that period to the peak power consumption for that period. The flatter the consumption

was for that period, the closer that value to 1. The more brief spikes and variability in consumption the

more that value would drop from 1. Load Shape is a less quantifiable metric associated with the general

profile of a customer’s power consumption for a given 24-hour day cycle. Some customers might have

very regular sine wave or square shaped cycles peaking during the day or during the night, and others

might have much more random variability. In addition, NOVEC provided the guidance that some

customers might see increases in power consumption increase in heating months, in cooling months, or

both or neither. Finally, NOVEC staff expressed to the team that while the goal is to improve accuracy, a

classification system that defined too many customer types would add too much complexity and

setup/processing time to their forecasting model and to err on the side of conciseness for class definitions.

With the above established parameters, the team approached the problem with two main goals:

establish the level of confidence in using BRC groupings to feed the prediction model, and if BRC

groupings were not self-consistent, define new class groupings and automate the binning of customers

into those groups. In order to accomplish these goals, the team wrote one set of java-based file crawlers

to calculate mean and standard deviation of the hour-specific usage within BRC groupings and another set

of crawlers to calculate metrics associated with seasonal, weekly and day-night cycle variation within

each specific customer agnostic of BRC membership. Customers that had similar variation on those three

time scales were grouped together using a third set of executables coded in java and evaluated again for

self-consistency. In general consumption behavior across each of the customers within each of the BRC

customer population data sets was found to be too varied to be useful as a grouping for forecasting.

Instead, the team defined 18 major classes and 6 sub-classes with different permutations of day-of-week,

seasonal, and day-night cycle variation. The six sub-classes were numbered similarly between the parent

class and sub class such that dual-peak and seasonally invariant classes are paired. The following table

shows a breakdown of how the team found the survey customers fell into each of those 24 groupings.

Count Class Description

Summer Total (MWh)

Winter Total (MWh)

Shoulder Total (MWh)

240 Class 01 7-Day-flat, Summer Peak, Day Heavy 150,417 105,657 135,300

9 Class 02 7-Day-flat, Summer Peak, Night Heavy 652 524 525

53 Class 03 7-Day-flat, Summer Peak, 24-flat 59,026 43,335 50,742

70 Class 04 7-Day-flat, Winter Peak, Day Heavy 6,917 7,641 4,822

58 Class 05 7-Day-flat, Winter Peak, Night Heavy 3,687 4,041 2,333

50 Class 06 7-Day-flat, Winter Peak,24-flat 6,607 7,694 4,521

133 Class 07 7-Day-flat, Dual Peak, Day Heavy 10,334 10,774 9,029

16 Class 08 7-Day-flat, Dual Peak, Night Heavy 756 839 745

43 Class 09 7-Day-flat, Dual Peak, 24-flat 7,479 7,674 6,922

41 Class 7.1 7-Day-flat, 365-flat, Day Heavy 52,126 38,919 40,744

5 Class 8.1 7-Day-flat, 365-flat, Night Heavy 725 535 483

49 Class 9.1 7-Day-flat, 365-flat, 24-flat 260,671 195,417 210,317

105 Class 10 5-2 Variant, Summer Peak, Day Heavy 65,520 44,792 59,105

1 Class 11 5-2 Variant, Summer Peak, Night Heavy 20 17 13

6 Class 12 5-2 Variant, Summer Peak, 24-flat 8,003 5,355 8,191

66 Class 13 5-2 Variant, Winter Peak, Day Heavy 44,374 46,369 29,637

5 Class 14 5-2 Variant, Winter Peak, Night Heavy 455 547 277

15 Class 15 5-2 Variant, Winter Peak, 24-flat 4,064 4,328 2,905

39 Class 16 5-2 Variant, Dual Peak, Day Heavy 6,880 7,342 5,509

3 Class 17 5-2 Variant, Dual Peak, Night Heavy 23 18 24

7 Class 18 5-2 Variant, Dual Peak, 24-flat 172 189 147

115 Class 16.1 5-2 Variant, 365-flat, Day Heavy 223,769 159,916 153,148

11 Class 17.1 5-2 Variant, 365-flat, Night Heavy 1,525 759 834

3 Class 18.1 5-2 Variant, 365-flat, 24-flat 2,468 1,958 1,547

The table above also provides the seasonal total megawatt-hours for each of the classes showing not only

the weighting by customer count within a class, but the breakdown of where the heaviest power

consumption falls across the existing classes.

The main body of this report provides additional detail on the methodology used to reach these

findings and additional metrics developed from class behavior during the post-processing phase of the

project. The appendices contain detailed instructions on how to reproduce the steps used to perform the

analysis so that as the NOVEC customer base grows and customer behavior evolves over time, the class

definitions and populations can be generated to update class assignments. The final appendix lists the

class assignment and filtered consumption ratios by time scale for each of the 1,150 survey meter

customers.

OR 699/SYST 699, NOVEC FALL, 2014

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Table of Contents

1 Introduction ........................................................................................................................................... 3

1.1 Project Background ....................................................................................................................... 3

1.2 Problem Statement ........................................................................................................................ 3

1.3 Objectives ..................................................................................................................................... 4

1.4 Statement of Work ........................................................................................................................ 4

1.4.1 Official Sponsors................................................................................................................... 5

1.4.2 Period of Performance .......................................................................................................... 5

1.4.3 Project Scope and Tasks ....................................................................................................... 5

2 Data Processing and Analysis ............................................................................................................... 6

2.1 Methodology ................................................................................................................................. 6

2.2 Pre-processing ............................................................................................................................... 7

2.3 Manual Inspection Tools ............................................................................................................... 9

2.4 Automated Binning ..................................................................................................................... 11

2.5 Evaluation of Automated Class Binning ..................................................................................... 13

3 Customer Category Analysis .............................................................................................................. 14

3.1 Class Assignment from BRC Origin Perspective ....................................................................... 16

3.2 Class Assignment from Class Destination Perspective ............................................................... 17

4 Conclusions ......................................................................................................................................... 21

4.1 Summary of Findings .................................................................................................................. 21

4.2 Lessons Learned .......................................................................................................................... 23

4.3 Future Work ................................................................................................................................ 23

5 APPENDIX A: Java Processing Source Code .................................................................................... 25

6 APPENDIX B: Java Processing User Guide ...................................................................................... 26

7 APPENDIX C: Excel Process File Assembly and Operation ............................................................. 32

8 APPENDIX D: Class Assignments and Output Artifacts ................................................................... 34


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List of Tables

Table 1 BRC Definitions .............................................................................................................................. 4

Table 2 NOVEC Raw Data Format .............................................................................................................. 7

Table 3 Census Data Format ......................................................................................................................... 7

Table 4 Merged File Column Headings ........................................................................................................ 8

Table 5 Merged Customer File Format ......................................................................................................... 8

Table 6 Customer Specific Average Normalizing ...................................................................................... 10

Table 7 Manual Inspection Observations .................................................................................................... 10

Table 8 Class Definitions ............................................................................................................................ 11

Table 9 Summer/Winter Peak Input............................................................................................................ 12

Table 10 Class Binning Counts and Power Consumption .......................................................................... 13

Table 11 BRC Source to Class Destination Matrix .................................................................................... 15

List of Figures

Figure 1 Hourly Manual Inspection .............................................................................................................. 9

Figure 2 Daily Manual Inspection ................................................................................................................ 9

Figure 3 Filtered Load Burden Formula ..................................................................................................... 12

Figure 4 Summer/Winter Peak Ratios ........................................................................................................ 12

Figure 5 Customer Counts by Seasonal, Weekly and Daily Load Characterizations ................................. 14

Figure 6 Summer Classes by BRC Origin .................................................................................................. 16

Figure 7 Winter Classes by BRC Origin ..................................................................................................... 16

Figure 8 Dual Peak Classes by BRC Origin ............................................................................................... 17

Figure 9 365-flat Classes by BRC Origin ................................................................................................... 17

Figure 10 Main Residential Class Destinations .......................................................................................... 18

Figure 11 Main Large Power Class Destinations ........................................................................................ 18

Figure 12 Main Small Commercial Class Destinations .............................................................................. 19

Figure 13 Lesser Small Commercial Class Destinations ............................................................................ 19

Figure 14 School, Field Lighting, Interruptible Class Destinations ............................................................ 20

Figure 15 Miscellaneous BRC Class Destinations...................................................................................... 20

Figure 16 Summer Monthly Usage for Summer Peak Classes ................................................................... 22

Figure 17 Shoulder Monthly Usage for Summer Peak Classes .................................................................. 22

Figure 18 Winter Monthly Usage for Summer Peak Classes ..................................................................... 23


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1 Introduction Northern Virginia Electric Cooperative (NOVEC) is a power wholesaler headquartered in Manassas,

Virginia. NOVEC provides power to in excess of 150,000 clients over different regions. NOVEC is

obliged to provide electrical power for any level of client demand. With that requirement as a boundary

condition, NOVEC buys power from retail power suppliers. Temperature changes, primarily amid the late

spring months, are a huge driver for expanded power request. NOVEC attempts to be as efficient as

possible in terms of the amount of energy purchased to decrease the energy cost for its customers.

1.1 Project Background

In order to minimize the amount of purchases without overcompensating with excessive bulk

purchases, NOVEC has developed a forecasting model that estimates future energy purchases. NOVEC

leverages forecast model insights to inform the volume of energy purchased (kilowatt-hours or kWh)

from suppliers. Economic metrics included in the model seek to characterize the basic load by capturing

economic growth or decline in the Northern Virginia area. The basic load is the energy requirement based

solely on the number of customers and their typical consumption, the magnitude of which changes with

time. To reasonably determine the power consumption rate of customers, local weather data is collected

and used to factor the effects of heating/cooling needs on historical energy purchases.

NOVEC prepares monthly long term (by month for 30 years) forecasts. They also provide daily short

term forecasts (for a period of three days). The extensive forecast period is important to accommodate the

complex capital planning, development, infrastructure efforts required to expand electric power capacity,

and delivery in accordance with federal and state regulations. The short term forecasting effects price

negotiation with the wholesale providers.

NOVEC’s models consider customer characteristics in addition to weather-related and economic

factors. The models currently include the following categorization of customers:

• Extremely High volume customers (aka “needle-movers”, ~5-10)

• Medium-to-low volume residential customers (~140,000)

• Medium-to-low volume non-residential customers (~12,000)

Historical monthly usage data (the basis of power bills) for each residential customer drive a baseline

load for a residential customer. The models then represent each non-residential customer as a multiple of

a residential customer. NOVEC assigns each customer a Billing Rate Code (BRC) that is used to drive

bill rates based on the expected consumption pattern of the customer. The models do not consider these

billing rate codes in the forecast calculations.

In 2011, NOVEC started a program to replace more than 1,100 regular meters with survey meters. A

survey meter captures hourly usage in kWh. NOVEC has supplied this survey meter data to the project

team along with the map coordinates and billing rate code associated with each meter.

1.2 Problem Statement

Currently NOVEC purchases power from power suppliers depending on the customer demand.

Usually there is an amount of power that has been purchased but hasn’t been consumed by any of

NOVEC’s customers. This causes an increase cost of providing power to customers, because they are

ordering more power than needed. NOVEC is developing a system that can better forecast customer

usage. They anticipate that categorizing customers in more than two categories, residential and non-

residential, can increase the accuracy of their load forecast.


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NOVEC is requesting that the project team provide a better algorithm for customer categorization that

can help in improve accuracy of demand forecasting. The table below depicts the BRC definitions and

counts of survey customers in each BRC bin provided as a starting point for evaluating existing customer

grouping. Note here that the Large Power description for LP types below is neither indicative of true

consumption volume nor meant to imply consideration as one of the needle-movers described above.

BRC Description Survey Meter Count

ES1 All Electric School 23

FL1 Athletic Field Lighting 23

IS Interruptible 8

IS1 Interruptible 1

LP Large Power 244

LP1 Large Power 88

LP2 Large Power 1

LP5 Large Power 17

1A1 Residential 4

RES Residential 461

RTO Residential - Time of Use 19

SC Small Commercial 1 Phase 85

SC1 Small Commercial 1 Phase 15





GMU Special Customer 1

HVA Special Customer 1

UNK UNKNOWN 39 Table 1 BRC Definitions

1.3 Objectives

The objective of this project is to examine the raw data that was provided from NOVEC and to

develop categories for its customers. Based on the analysis of the data the team is to provide NOVEC

with a number of categories for NOVEC’s customers and assign these categories to each customer in the

given survey meter population. Another primary goal is automation of the assignment process. The

categories that are developed and given to NOVEC will be inputs into an existing SAS forecasting model,

which NOVEC currently uses. These categories will provide a more accurate and precise forecast for

NOVEC in the future. The new categories will substitute the current two categories that are being used by

NOVEC.

1.4 Statement of Work

The deliverables for this project include this final report, the attached files consisting of JAVA

preprocessing source code and intermediate and post-processing Excel analysis files were requested from

NOVEC to support them in their future work. Additionally, the team will deliver their findings in

presentation format on December 12, 2014 at a George Mason University facility.


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1.4.1 Official Sponsors

The primary stakeholder in this project is NOVEC who is the project sponsor. The primary points of

contact at NOVEC are Bryan Barfield and Bob Bisson, who will be working with the project team and

providing the required information for them. As representatives of the NOVEC customer base, Mr.

Bisson and Mr. Barfield, expressed the need for better load purchasing forecasts in order to minimize cost

to the consumer. As the project advisor, Prof. Hoffman represents George Mason University’s interest in

maintaining a good working relationship with NOVEC to continue an ongoing history of mutually

beneficial projects involving both parties.

1.4.2 Period of Performance

All aspects of this project took place within the 2014 GMU Fall semester from 8/28/14 – 12/12/14.

The analysis performed was summarized at the conclusion of the project on 12/12/14 with a briefing

presented by the project team on the main GMU campus. Every effort to enable future reuse of the

analysis tools created for the project for future studies will be made by the project team to provide

technical support remotely.

1.4.3 Project Scope and Tasks

The project is divided into two parts. First, the team will devise an algorithm that can categorize

NOVEC’s current and prospective customers into multiple categories. The team will consider billing rate

code and the survey meter data in devising this algorithm. The use of these categories will help NOVEC’s

current forecasting system to better forecast load, which will lead to decreasing the cost of energy

purchased from other power suppliers. We intend to categorize the customers via data regression and

clustering. Data regression and clustering discovered around the major trend analysis will be used by

NOVEC personnel after the completion of this project to feed additional independent variables in their

current forecasting models. In the second phase, provided sufficient time is remaining after the first

phase, the team will investigate ways to correlate geographic grid data with public domain information to

provide further approaches to categorize customers based on their location.

Final evaluation of the quality of the definition and assignment of classifications to customers relies

on the integration of those assignments with an improved forecasting model. The scope of this project

covers neither integrating improvements to the current prediction model, nor assessment of how well the

new class definitions will scale from the survey meter population to the global customer population of

150,000+ customers. It is the hope of the project team that future evaluation of metrics involving

improved accuracy of forecasting be communicated by the stakeholders when the data become available.

Based on discussions late in the project with NOVEC stakeholders, they communicated that while they

were interested in quantitative analysis of the outcome of classification, the actual assignment pairings

were of less value to them than a well-documented approach for repeating this type of analysis in the

future.


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2 Data Processing and Analysis

The following sections will describe the general approach including preprocessing of data necessary

to begin analysis, and application of a Filtered Load Burden (FLB) [further defined in section 2.4] to

quickly bin customers into one of 24 Classes. Following that, these sections describe verification checks

calculated with normalization factors to remove magnitude from consideration and verify consistent Class

assignment of customers with respect to 24-hour load shape and weekly/annual behavior. This section

will also introduce terms developed by the analysis team to create derived variables and metrics with

which to compare and contrast customers within a BRC and evaluate correctness of BRC assignment for

each customer. The last section of the Data Processing portion of this document will provide a brief

description of spreadsheet tools developed in MS Excel to manually inspect customer behavior on a year-

over-year time scale as well as daily variation viewed in 2-week spans. These post-processing

spreadsheets allow the user to conduct spot-inspection verification against false-positives and other errors

in matching and enable the user to identify customers whose power usage can best be described as

“unpredictable”.

2.1 Methodology

The general approach for this project entailed developing automated processes to evaluate customer

power usage from historical data. The goal of analyzing power usage behavior was to create-self

consistent customer classifications based on either the consistency of behavior within existing NOVEC-

assigned BRC binning or the team’s own analysis-driven classification. The following sections will

describe a four-phase approach to data transformation and analysis.

- Phase 1: Preprocessing the team created a Java file crawler to transform data originally

gathered as separate monthly files with customer data interleaved within the file into a tabular

listing with time axis and customer Ids in rows and columns, respectively.

- Phase 2: Manual Inspection Tools the team created macro-driven Excel files to rapidly evaluate

the behavior of individual customers on yearly and daily/weekly time scales focusing on one

BRC at a time

- Phase 3: Automated Binning the team created a Java file crawler to evaluate the tabular files

from Phase 1 filtering the data on different time scales to find proportionality in the peaks and

valleys of power usage variation.

- Phase 4: Evaluation of Bins the team created a Java crawler to extract customer data from BRC

tables and reassemble the data into Class-specific files. The Class-binned files were scaled and

normalized to remove magnitude as a consideration and evaluated for standard of deviation

across individual time slices. Finally the team conducted a manual inspection of behavior using

the same Excel files created in Phase 2 to search for any outliers not exposed by standard

deviation measurements

The team spent the remaining portion of the project documenting procedures for using the Java

executables and Excel files in order to facilitate future use by NOVEC stakeholders. While the

classifications produced by this project may be correct at this point in time, customer behavior can change

over time and new customers will be added. The final judgment of the new classification will depend on

whether the refined inputs to the purchasing model will produce more reliable predictions. While more

rigorous analysis is possible in determining the fit of the automated class binning, the time restrictions on


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this project did not afford us time to apply Fourier transforms to verify length of regular cycles. Using the

methodology described above (specifically phase 2 and 4) we quickly observed that customers in the

same BRC would often exhibit vastly different consumption patterns. Consequently, the methodology

section will largely focus on the secondary goal of developing new groupings taking as given that BRCs

were not valuable for predicting power usage.

2.2 Pre-processing

With the significant volume of data presented to the team, the primary focus of this project has been

automation. To that end, several Java executables were created to take file folder locations as inputs and

produce data reassembled and updated with variables to drive the analysis process. The main data

transformations were from Raw Data to BRC-binned data as well as the integration of grid location and

time-derived variables. The original expectation was that the BRC groupings would provide a reasonable

starting point for classifying customers by performing a clustering analysis around power usage behavior.

The raw data made available to the team took the form of individual monthly files with customer Id

values repeated row-over-row and reading date/time and kilowatt values listed in the next two columns as

illustrated in the table below

Customer ID Reading Date/Time KW Usage Customer 1 01JAN2011 00:00:00 62.1

… … … Customer 1 31JAN2011 23:00:00 48.2 Customer N 01JAN2011 00:00:00 58.7 … … … Customer N 31JAN2011 23:00:00 43.9

Table 2 NOVEC Raw Data Format

NOVEC stakeholders communicated to the team that while maintaining an emphasis on customer

privacy issues, they could also provide anonymized data about the customers including their original BRC

and grid-location on a map made available to the team. While location data proved unnecessary at this

stage, the BRC listed in the RATE_SCHED column provided a good starting point for analysis of

customer groupings and this report will cover possibilities for future enhancements involving grid data in

a later section. The census data format is shown in the table below and the full census data showing

individual customer ID pairings with BRC is available in Appendix D:

CUSTOMER_ID MAP_GRID RATE_SCHED

Customer 1 212-18 ES1

… … …

… … …

Customer N ###-## [value] Table 3 Census Data Format

Based on discussions with NOVEC, the team had also anticipated that the largest variations of usage

for most customers were differences between heating, cooling and off months as well as day of week

versus hour of day. In addition to transforming the data into tabular format, the team developed the

preprocessing code to write out columnar data with the time-specific variables in order to facilitate post-


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processing in later executables outlined in the table below. Ordinal days of the year/month/week (in

integer format) were not used, but are included in intermediate files as possible feeders to Fourier analysis

in future studies.

Column Heading Variable description

ACTIVITY

Activity associated with this hour of the day. Possible values: waking, working, evening, overnight. The anticipation was that analysis of peaks in different time windows within a day would reveal intuitive conclusions about customer behavior. These windows would be later collapsed to just daylight and night hours.

ORD DAY Ordinal day of the year. Not used

DOM Ordinal day of the month. Not used

DOW Ordinal day of the week. Not used

BUSINESS Weekday or weekend as well as holidays captured through hard-coded calendar day matching against 10 observed federal holidays

MONTH Ordinal month of year. Not used

SEASON Summer, Winter, or Shoulder (i.e. Spring, Autumn combined)

YEAR Ordinal year. Not used

TYPICAL Arithmetic mean across all N customers in a BRC for a specific time coordinate, see Table 5 below. The term typical will become important in discussing Hourly Departure from Typical (HDFT) on page 8 below.

STD DEV Standard deviation across all N customers in a BRC for a specific time coordinate, see Table 5 below

Table 4 Merged File Column Headings

After preprocessing the table format were written out in BRC-specific files with the format shown in

the table below:

Reading Date/Time Cust 1 ... Cust N Time-related variables Typical Std Dev

01JAN2011 00:00:00 62.1 58.7 60.4 60.4 …

31AUG2014 00:00:00 112.0 122.0 117.0 117.0

Table 5 Merged Customer File Format

An additional preprocessing Java tool was created to aggregate hourly data points into daily data

points, summing the values captured in each 24-hour cycle. Once these customer-merged tables saved as

individual BRC-related files were written out, the next phase of manual tool inspection could begin. The

team developed macro-driven Excel files to facilitate this process as described in the next section. All

raw files and pre-process output can be found on the project website hosted by George Mason University

as described in Appendix C. Early evaluation of the data involved counting the number of data points a

customer spent more than two standard deviations from the Typical value for that time index. We labeled

this metric the Hourly Departure from Typical (HDFT), but quickly found it to be only just informative

enough to say that there were a significant number of customers that spent a large portion of their time

behaving differently from the BRC-typical value. A similar evaluation of daily data (Daily Departure

from Typical or DDFT) revealed that we had quantifiable results to conclude that BRC groupings were


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not self-consistent enough to warrant use in the prediction model. The next step was to evaluate customer

behavior by inspecting graphical representation of power usage customer-by-customer to develop a best

guess as to useful category properties.

2.3 Manual Inspection Tools

Early meetings between the project team and NOVEC stakeholders highlighted the expectation that

customer behavior typically varies in one of three ways: seasonally, by hour of day (Load Shape), and by

day-of-week. While this provided an intuitive context with which to analyze the data, the team was given

the additional bounding condition that fewer behavior classifications were better as the greater the number

of independent variables, the more complex the purchase prediction model becomes. Rather than taking

an iterative approach of guessing at useful permutations of the three independent variables, the team

needed a way to visually inspect the large volume of data. The approach involved creating a series of

macro-driven Excel spreadsheets to inspect each customer. The buttons illustrated in the screen captures

below allow the user to quickly scroll through columnar data with an additional button/macro to scroll

through row-wise data showing hourly behavior in two-week spans.

Figure 1 Hourly Manual Inspection

Figure 2 Daily Manual Inspection


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In each spreadsheet, the spinner button(s) at call-out A drive the row/column offset at B used to feed

an Address() Excel function call at C to retrieve data in the supporting worksheet (also referenced in B)

using the Indirect() Excel function calls at D to dynamically populate the chart shown. These files are

further documented in APPENDIX C: Excel Process File Assembly and Operation. It should also be

noted that in order to allow multiple customers to be graphed on the same vertical axis, a normalizing and

scaling factor was applied to remove magnitude from consideration. The team developed the concept of a

Customer Specific Average (CSA) which calculates the full-1300-day-historical average for each

customer and normalizes the merged files with each KW output column written to new files pegging 1.0

to the CSA for that customer Id.

Reading Date/Time Cust 1 ... Cust N Time-related variables Typical Std Dev

01JAN2011 00:00:00 1.1 0.5 1.1 0.2

…

31AUG2014 00:00:00 1.5 1.2 1.3 0.17 Table 6 Customer Specific Average Normalizing

Herein the Typical and Standard Deviation values are recomputed for convenience with the same

approach to a row-specific value for each time-coordinate.

The primary benefit of the manual inspection process was an intuitive grasp of how many “species”

of Load Shape were exhibited by the data and how many variations of only-summer-high, only-winter-

high, and winter/summer-combined high there were. In short the numbers of possible values of each of

the three independent variables were quickly hinting at a high number of permutations that might drive a

proposed class structure. In order to narrow the focus of the final classification system, the team

determined that there were 10 primary observations worthy of consideration within the permutations of

the independent variables as delineated in the table below.

Observations Caveats

Summer High - Weekday high/Weekend Low (5-2 Variant) as well as flat 7-day very common - Weekend high/Weekday low (2-5 Variant) rare - Some residential properties exhibit non-cyclical day of week or Thursday-Sunday high (assumed to be captured by 5,2 Variant with low threshold) - Seasonal and Day/Night variation is highly sensitive to boundary placement - Without collapsing Season/Weekday subgroups permutation count jumps to 36

Winter High

Summer/Winter High

Seasonally Flat

Day Heavy

Night Heavy

24-Flat

5-2 Variant

7-day Flat

Other Table 7 Manual Inspection Observations

Recall now that NOVEC stakeholders desired to develop a classification system that allows for

separation of distinctive behavior without overloading the predictive model with an unwieldy amount of

input data. Given the above observations, the team realized that a 4x3x3 permutation of input variables

would lead to 36 classifications and decided to narrow the focus of class inputs. The team weighed the


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rarity of certain observations with the possibility of attaching additional attributes to a customer Id (such

as assigning them seasonal behavior load multipliers) to determine that Seasonally Flat or Seasonally

Variant are distinctive enough on that time scale. Similarly, despite observing the occasional customer

that would have 4 or 6-out-of-7 days of high usage per week, or residential customers with spikes on

seemingly random days, weekly variation was narrowed to 5,2 Variant and 7-day Flat. The expectation

there being that unpredictable could only best be predicted as hoping that the daily average would be

close enough. Finally casual observation shows all 4 permutations of summer only highs, winter only

highs, and summer/winter combined highs as well as seasonally flat data. Consequently, final prediction

data as fed to SAS will require additional independent variables to drive actual seasonality and daily

variation.

2.4 Automated Binning

Leveraging the observations in the Manual Inspection phase of analysis the team developed the

classification system shown in the table below, based on permutations of the three independent variables

collapsed as described in the previous section.

Class Class Description

Class 01 7-Day-flat,Summer Peak, Day Heavy

Class 02 7-Day-flat,Summer Peak, Night Heavy

Class 03 7-Day-flat,Summer Peak, 24-flat

Class 04 7-Day-flat,Winter Peak, Day Heavy

Class 05 7-Day-flat,Winter Peak, Night Heavy

Class 06 7-Day-flat,Winter Peak, 24-flat

Class 07 7-Day-flat, Dual Peak, Day Heavy

Class 08 7-Day-flat, Dual Peak, Night Heavy

Class 09 7-Day-flat, Dual Peak,24-flat

Class 7.1 7-Day-flat, 365-flat, Day Heavy

Class 8.1 7-Day-flat, 365-flat, Night Heavy

Class 9.1 7-Day-flat, 365-flat, 24-flat

Class 10 5-2 Variant, Summer Peak, Day Heavy

Class 11 5-2 Variant, Summer Peak, Night Heavy

Class 12 5-2 Variant, Summer Peak, 24-flat

Class 13 5-2 Variant, Winter Peak, Day Heavy

Class 14 5-2 Variant, Winter Peak, Night Heavy

Class 15 5-2 Variant, Winter Peak, 24-flat

Class 16 5-2 Variant, Dual Peak, Day Heavy

Class 17 5-2 Variant, Dual Peak, Night Heavy

Class 18 5-2 Variant, Dual Peak, 24-flat

Class 16.1 5-2 Variant, 365-flat,Day Heavy

Class 17.1 5-2 Variant, 365-flat,Night Heavy

Class 18.1 5-2 Variant, 365-flat,24-flat Table 8 Class Definitions

Note that as described above, sub-types of hour-of-day variation have been collapsed to 5-2 Variant

(weekday and weekend) or 7-Day-flat as a catch-all for both truly invariant through day of week.

Additionally the numbering of the classes was defined to closely associate Dual Peak seasonal variation


12

with flat usage through the year. It should be noted that 5-2 variation is meant to capture both weekday-

high/weekend-low behavior as well as the reverse of weekday-low/weekend-high behavior. In order to

keep total defined classes to a minimum and still accurately predict customer usage, ultimately the FLB

for each customer associated with each time scale will have to be included as inputs to prediction models.

The team expected that with the class definitions in the table below, there would be a certain number of

customers that had nearly unpredictable behavior. Statistically these customers will often fall into the flat

classes with some number appearing as false matches with another existing class. Rather than create

additional classes to capture the difference between a true match and a statistical anomaly, the team took

the approach of making the distinction during post-processing. Table 8 above depicts the initial class

definitions proposed by the team. We recommend that outliers identified through the HDFT metric be

treated separately during load forecasting, but did not create a labeled class to make this distinction.

With these classifications in mind, the team needed a metric that would quantifiably measure

alignment of usage behavior for a customer with one of these Classes. Drawing inspiration from the Load

Factor described in the Project Background section, the team developed the FLB metric mentioned earlier

that calculates the ratio of the quantity of a filtered average usage against a given time variable relative to

quantity of the complement of that usage and number of data points in the complement data set. FLB can

be calculated as shown in the figure below.

Figure 3 Filtered Load Burden Formula

Additionally another metric was created to capture the relationship between FLB and the seasonal

variation of Summer, Winter or Dual peak behavior as well as Flat usage throughout the year. These

permutations of seasonal variation were quantified with the formula in the figure below and adapted to the

post-processing logic described below

Figure 4 Summer/Winter Peak Ratios

Summer Peak Winter Peak Class Input

True True Dual Peak

True False Summer Only

False True Winter Only

False False 365-Flat Table 9 Summer/Winter Peak Input

With the expectation that customers would fall into one of the permutations of Class behavior by

being either close to 1.0 on this metric or some value departed from 1.0 the team developed another Java


13

crawler to calculate these metrics. The output table was then pulled into Excel and logic developed to

determine the most appropriate threshold values for departure from 1.0 as well as the permutation logic

and lookup functions to assign the classification and count the results. The logic is briefly outlined in

APPENDIX C: Excel Process File Assembly and Operation.

2.5 Evaluation of Automated Class Binning

The team used a combination of manual visual inspection and application of the HDFT metric to class

groupings to evaluate correctness of assignment. Ultimately we chose to reuse the tools developed in

earlier stages by creating new Java executables to reassemble the data in files grouped by class and

creating new Excel spinner files to manually inspect graphs of power usage by customer. With the

exception of Class 8.1 members, the automated binning produced a high percentage of valid assignments.

Several members automatically binned as Class 8.1 showed by inspection as Winter Heavy instead of 365

flat and were moved manually. All summary tables included in this report reflect that change. The table

below shows the final binning and the megawatt totals by season for the individual classes.

Count Class Description

Summer Total (MWh)

Winter Total (MWh)

Shoulder Total (MWh)

240 Class 01 7-Day-flat, Summer Peak, Day Heavy 150,417 105,657 135,300

9 Class 02 7-Day-flat, Summer Peak, Night Heavy 652 524 525

53 Class 03 7-Day-flat, Summer Peak, 24-flat 59,026 43,335 50,742

70 Class 04 7-Day-flat, Winter Peak, Day Heavy 6,917 7,641 4,822

58 Class 05 7-Day-flat, Winter Peak, Night Heavy 3,687 4,041 2,333

50 Class 06 7-Day-flat, Winter Peak, 24-flat 6,607 7,694 4,521

133 Class 07 7-Day-flat, Dual Peak, Day Heavy 10,334 10,774 9,029

16 Class 08 7-Day-flat, Dual Peak, Night Heavy 756 839 745

43 Class 09 7-Day-flat, Dual Peak, 24-flat 7,479 7,674 6,922

41 Class 7.1 7-Day-flat, 365-flat, Day Heavy 52,126 38,919 40,744

5 Class 8.1 7-Day-flat, 365-flat ,Night Heavy 725 535 483

49 Class 9.1 7-Day-flat, 365-flat, 24-flat 260,671 195,417 210,317

105 Class 10 5-2 Variant, Summer Peak, Day Heavy 65,520 44,792 59,105

1 Class 11 5-2 Variant, Summer Peak, Night Heavy 20 17 13

6 Class 12 5-2 Variant, Summer Peak, 24-flat 8,003 5,355 8,191

66 Class 13 5-2 Variant, Winter Peak, Day Heavy 44,374 46,369 29,637

5 Class 14 5-2 Variant, Winter Peak, Night Heavy 455 547 277

15 Class 15 5-2 Variant, Winter Peak, 24-flat 4,064 4,328 2,905

39 Class 16 5-2 Variant, Dual Peak, Day Heavy 6,880 7,342 5,509

3 Class 17 5-2 Variant, Dual Peak, Night Heavy 23 18 24

7 Class 18 5-2 Variant, Dual Peak, 24-flat 172 189 147

115 Class 16.1 5-2 Variant, 365-flat, Day Heavy 223,769 159,916 153,148

11 Class 17.1 5-2 Variant, 365-flat, Night Heavy 1,525 759 834

3 Class 18.1 5-2 Variant, 365-flat, 24-flat 2,468 1,958 1,547 Table 10 Class Binning Counts and Power Consumption


14

3 Customer Category Analysis In this section, we present the assignment of survey customers into classes using the methodology

described in Section 2. Specifically, we discuss and present key results to answer the following questions:

● How do the survey customers break out across the seasonal, weekly and daily load shape

characterizations?

● How many customers are in each class?

● What is the distribution of class in each BRC?

● What is the distribution of BRC in each class?

● What is the distribution of monthly kWh usage for each class, by peak/non-peak period?

● How consistent are the load shapes and magnitudes within each class?

Figure 5 shows the number of survey customers by possible combination of seasonal, weekly, and daily

shape characterization. In each display, daily shape characterizations appear in columns. Rows

correspond to seasonal-by-weekly or vice-versa. Key observations include:

● Most survey customers:

○ use more power during the summer

○ use power consistently throughout a week

○ use more power during the day

● There are at least twice as many Summer Peak customers than Dual Peak, Winter Peak or 365-

flat customers, which are approximately equally likely.

● Flat usage throughout the week is approximately twice as likely as usage that varies between

weekday/weekend.

● Approximately 70% of survey customers use more power during the day

These observations follow intuition regarding electric power usage, especially regarding heating and

air conditioning. Air conditioners draw significant electric power during the summer months. During this

time, consumers typically use air conditioning consistently all week, and the systems work harder during

the day when the temperature is higher. During winter months, heating systems increasingly rely on non-

electric power such as natural gas or propane

Figure 5 Customer Counts by Seasonal, Weekly and Daily Load Characterizations

Table 11 presents a matrix of BRC to class. Each cell in the matrix is the number of customers

assigned to the class (the row) from the BRC (the column). Key observations include:


15

● Aside from BRCs with small survey customer populations, each BRC mapped to at least 5 and

usually more, classes. This indicates significant load shape variation within a BRC, and confirms

the findings from earlier in the project that ultimately led to the load shape classes, the FLB

metric and class assignment method.

● Aside from Class 11, each class draws survey customers from more than one BRC. This means

customers with similar load shapes have different BRCs. Since BRC drives kWh pricing at

NOVEC, and pricing is sensitive to load shape, NOVEC might use this information to move

customers between BRCs or adjust rates to better align pricing with load shape.

7-day-flat 5-2 Variant

Summer Winter Dual Flat Summer Winter Dual Flat

D N F D N F D N F D N F D N F D N F D N F D N F

Class 01 02 03 04 05 06 07 08 09 7.1 8.1 9.1 10 11 12 13 14 15 16 17 18 16.1 17.1 18.1

ES1 1 1 1 1 11 1 7

FL1 2 1 1 1 1 5 10 2

IS 3 1 1 3

IS1 1

LP 66 2 21 7 4 4 7 2 7 21 2 14 30 1 1 22 1 3 5 23 1

LP1 3 3 3 1 2 1 1 2 8 8 1 55

LP2 1

LP5 4 1 2 1 2 2 5

1A1 1 1 2

RES 135 5 18 46 9 24 108 9 24 6 1 32 1 8 1 7 19 2 4 1

RT0 2 1 1 2 3 6 3 1

SC 4 2 4 2 22 2 1 2 2 23 5 1 3 1 8 2

SC1 1 2 2 6 1 1 1

SC4 20 2 3 13 4 2 6 2 1 16 1 7 4 7

SC5 4 2 4 2 1 2 1 1

SC2 1 1 1 1 1 1

SC6 1 1 1 1 1

GMU 1

HVA 1

UNK 4 2 1 2 2 2 2 3 4 5 2 2 1 6 1

Total 240 9 53 70 58 50 133 16 43 41 5 49 105 1 6 66 5 15 39 3 7 115 11 3

Table 11 BRC Source to Class Destination Matrix


16

3.1 Class Assignment from BRC Origin Perspective

In this section, we describe the BRC population of each class with more than 20 survey customers

through a series of consistently-formatted histograms. The bins in each histogram represent the possible

BRCs, and the vertical bars represent the count of survey customers from that BRC assigned to the class.

The histograms appear below as Figure 6 through Figure 9

Figure 6 Summer Classes by BRC Origin

Figure 7 Winter Classes by BRC Origin


17

Figure 8 Dual Peak Classes by BRC Origin

Figure 9 365-flat Classes by BRC Origin

3.2 Class Assignment from Class Destination Perspective

In this section we assess the number of customers being assigned to each class in histograms

according to similar BRC origins. NOVEC’s challenge in assigning customers to non-hourly-survey-

meter sites will be in correlating private customer data with likelihood of behavior in each of the class

categories. For the following histograms not that groupings of three classes (1-3, 4-6, etc.) follow a

repeating day-heavy, night-heavy, 24-hour-flat cycle. The variation on longer time scales for each class is

annotated on the individual histograms.


18

Figure 10 Main Residential Class Destinations

Note that the Residential customers largely fall into the Day-Heavy, 7-day-flat classes for both

Summer and Dual Peak, with a smaller portion falling into Winter Peak.

Figure 11 Main Large Power Class Destinations

Note that an overwhelming portion of Large Power customers fall into the Summer, 7-day-flat, day-

heavy class, and a significant number fall into the day-heavy classes for 5-2 Variants of both 365-flat and

summer-peak.


19

Figure 12 Main Small Commercial Class Destinations

Figure 13 Lesser Small Commercial Class Destinations

Note that the small commercial customers tend to group in the winter-peak bins.


20

Figure 14 School, Field Lighting, Interruptible Class Destinations

Figure 15 Miscellaneous BRC Class Destinations


21

4 Conclusions Relating to the two main objectives, the team can draw definitive conclusions about the usefulness of

BRCs as a predictor of power consumption and the application of software to create and refine

quantitative, automated approaches to finding trends in power consumption. This section also documents

a summary of other findings, our lessons learned, and ideas for future work. The number of outlier points

relative to an hourly-calculated standard of deviation of each customer data set within BRC groupings

shows undeniably that a forecasting model must use other attributes to predict power consumption. The

set of classes proposed may not be the final iteration of customer classification, but it will at least provide

a new analytical approach for conducting future clustering analysis. The sizes of populations in each

class seem to follow intuition in that:

- Summer-peak classes follow from air conditioning being a universal necessity for residential,

government and commercial buildings.

- Winter- and dual-peak classes represent a mix of customers that either have gas heating or

electric heat pumps.

- Small Commercial and a subset of Large Power BRCs tend to be the customers that are Night

Heavy (in addition to the obvious Field Lighting candidates.

- Electric School and Small Commercial BRCs tend to be the customers that exhibit 5-2 variant

behavior

4.1 Summary of Findings

Based on the project work, we offer the following findings:

1. BRC alone cannot be used to accurately classify customers by load shape or usage magnitude.

2. The proposed method accurately classifies customers by load shape in most cases.

3. Survey customers with similar load shapes are spread across BRCs

4. The survey customer population has load shape trends that mirror common-sense intuition.

NOVEC chose survey customers using a structured random sampling intended to represent the

overall population. Taking these facts together, we find that the survey customer population is

likely to be representative of the overall NOVEC population.

5. A significant majority of survey customers with seasonal peaks tend to use less than 2500

kWH/month during those peak periods.

6. There is wide variability in monthly usage patterns for survey customers without recognizable

seasonal peaks.

7. Hourly meter readings provide valuable insight into customer load shape.

8. 95% of customers had low HDFT within a class

9. <1% of customers had both high HDFT and were high-volume power consumers

10. Outlier customers that presented large HDFT were typically skewed by large step-wise transitions

in power behavior

Note that findings 3 – 7 are based on the proposed FLB metric, class structure and classification

method and the results of applying that method to the survey customer sample.

In addition we offer the following observations. Power consumption levels in the survey meter

population were heavily skewed towards a small subset of high-volume customers. The top 50 power


22

consumers in the 1,150-member population used more power than the bottom 1,100 combined. Of the

top 10 power consumers, only 2 appeared in the most populous of the 24 classes and the other 8 did not

fall into any of the 5 most populous classes. The number-one power consumer and 3 others in the top 10

were categorized as 24/7/365 flat (i.e. no variance) and only two showed a strong 5-day-on, 2-day-off

business-like variation. Because the low-volume power consumers represent a much greater majority of

the population than high-volume, these customers represent a greater potential for savings in purchase

forecasting. The following figures represent an example subset of distributions of monthly power

consumption during different seasons by the summer-heavy classes (chosen because they were the most

populous). Note here that Class 01 is 7-Day-flat,Summer Peak, Day Heavy; Class 03 is 7-Day-flat,

Summer Peak, 24-flat; Class 10 is 5,2 Variant, Summer Peak, Day Heavy.

Figure 16 Summer Monthly Usage for Summer Peak Classes

Figure 17 Shoulder Monthly Usage for Summer Peak Classes


23

Figure 18 Winter Monthly Usage for Summer Peak Classes

4.2 Lessons Learned

The team offers the following lessons learned based on our experiences with the project:

If a project has a decision point, strive to make that decision as early as possible. If we had

eliminated BRC earlier in the process, we would have had more time to work on the FLB

approach.

Flexibility in tools/languages increases the productivity of the team, as team members can work

in parallel using familiar technologies (e.g. Java, Excel, R).

Simpler is better – NOVEC motivated the team to consider fewer classes. This led to a simple

metric and scheme that produced good results.

The team acknowledges that the analysis indicated fairly early that BRC could have been recognized

as unusable for forecasting, but efforts continued at changing the metrics used in hopes of forcing the data

to bear out a different conclusion. Accepting this outcome earlier would have afforded more time for

post-processing of Class assignments and analysis of the correctness of those pairings. The best way to

have avoided this would have been to start focusing on development of alternative metrics earlier.

Ultimately, because the data was so difficult to visualize the team would have also benefitted from

additional prior experience in data visualization techniques and would have probably developed the

spinners described in Appendix C sooner.

4.3 Future Work

We expect a significant amount of work will be required to realize the benefits of refined accuracy in

power consumption prediction, from these class pairings but the foundation is there. Members of the

NOVEC staff in particular will need to associate knowledge about customer characteristics (pre-

anonymized) with power consumption behavior to be able to scale the survey meter population data up to

the global population of customers for whom there only exists monthly consumption data points.

Surprising outcomes like the large number of only-winter-peak class types will need to be explained. The

team offers the following ideas for future work:


24

1. Suggest NOVEC explore methods for capturing hourly or daily meter readings from a broader

population. Ideas include:

a. Institute/encourage self-reporting of hours of operation and weekly operation schedule.

b. While the project team was only provided geographic grid data to address privacy concerns,

the data available to NOVEC staff (geographic grid data, address, type of business, etc.)

could be used to help correlate non-survey-meter customers’ known characteristics with a

likely class assignment

2. Integrate the proposed classification method into the long-range forecasting model (i.e. replace

residential vs. commercial with class 1…N. This would include, at a minimum:

a. Define and implement a method to extrapolate survey customer classification results to the

broader population for integration into the long-range forecasting model. Specifically, define

a mechanism to determine the expected number of customers in each class such that the total

reflects the entire expected NOVEC customer base.

b. Define and implement monthly usage estimates for the classes implemented in the long-range

forecasting model.

3. Possible improvements pre-processing and analysis tools:

a. Excel Spinner improvements to read exterior CSV files and reduce on copy/paste operations

b. Java code improvements to help automate setting up of SAS model inputs and replace all

hard-coded time region borders, holidays, etc. with configurable external data files

4. The classification system itself could be improved by introducing Fourier transform frequency

analysis applied to filtered data to find regularity on hourly and daily scales not captured by FLB

metrics. Spikes at 24-hour, 12-hour and 6-hour time scales could automate the identification of

some of the regularly repeating load shapes observed. Filtering the data by day of week and

season before applying frequency calculations could identify customers that appear flat during

some time periods and more variant over other time periods.

5. Casual observation revealed a small subset of customers that change day-of-week and day/night

cycle behavior throughout the year. This evidence implies a future need to update the

classification system with a season index and direct feed of seasonal Filtered Load Burden to the

prediction model in correlation with weather forecasting. The originally proposed 24-class

system could be reduced to 6 season-agnostic baseline classes and expanded out to cover more

granular definitions of hour-of-day and day-of-week behavior than the proposed 5-2 Variant/Flat

or Day/Night/Flat coarse values. The number of gradations of each time scale would increase the

number of class definitions again, but might provide more accuracy.

6. Additional post-processing of class behavior trends could link amplitude of variation

(proportional to FLB) on one time scale (season, day-of-week, or hour) with consumption

variation on one or both of the other two time scales. In other words, if most customers with 5-2

variation weekly and summer-only peak behavior had consistent amplitude of variation day

versus night, that trend could be used to assess probability of similar behavior in the larger non-

survey-meter customer population.

In closing, the project team would like to thank our sponsors at NOVEC for providing the opportunity

to work on a very interesting project and also to thank our faculty advisor, Professor Hoffman for

providing the guidance and access to resources that helped us deliver a better final product.


25

5 APPENDIX A: Java Processing Source Code The Java processing source code for this project can be found at http://seor.gmu.edu/projects/SEOR-

Fall14/NOVEC/deliver.html and the relevant files are described below with full user operation instructions

found in Appendix B

Class File Name Description

BoundCustomers.java A directory crawler used to normalize the power consumption

data to a Customer-Specific Average (CSA) scale rather than

watts.

BoundDailies.java A directory crawler used to normalize the power consumption

data to a Customer-Specific Average (CSA) scale rather than

watts and also aggregate each 24-hour cycle to a daily

consumption sum.

FilteredLoadFactor.java An object class used to represent a collection of Filtered Load

Burden (FLB) metrics across different time scales

FilteredLoadFactorCrawler.java A directory crawler used to output FLB metrics across

different time scales

HourlyDepartureFromTypicalClass.java A directory crawler used to output HDFT metrics across

different time scales

KWDatum.java An object class used to represent a collection of power usage

data points with raw and normalized values

MergeClassDailyNorm.java A directory crawler used to concatenate single-customer daily

usage data point files into a single class-grouped file.

MergeClassNorm.java A directory crawler used to concatenate single-customer hourly

usage data point files into a single class-grouped file.

MergeCustomersNorm.java A directory crawler used to concatenate single-customer hourly

usage data point files into a single BRC-grouped file.

MergeCustomersDailyNorm.java A directory crawler used to concatenate single-customer daily

usage data point files into a single BRC-grouped file.

NovecCustomer.java An object class used to represent name, BRC/class and

geographic grid information about a NOVEC customer

ProcessData.java A directory crawler used to merge non-continuous data files

into single-customer history files.

ReclassifyNorm.java A directory crawler used to rename single-customer data files

to match newly assigned class groupings.

http://seor.gmu.edu/projects/SEOR-Fall14/NOVEC/deliver.html



26

6 APPENDIX B: Java Processing User Guide The following steps are meant to allow the user to leave a snapshot of data from different perspectives

along the way in order to debug aggregation logic and inspect the data for insights. Java JDK SE 1.7 or

later is required.

1. ProcessData.java

1.1. Requirements: Folder organized with the following structure and naming convention

- Root Folder of arbitrary name

- Sub Folder named exactly “1CensusData”

- Sub Folders named with the convention “%SurveyData[Year of collection]” with contiguous

years in order to maintain correct scanning logic

1.2. Command line execution:

1.2.1. Change directory to the src folder in project file

1.2.2. Compile code with the command

javac edu\gmu\NOVEC\model\NOVECCustomer.java

edu\gmu\NOVEC\preprocess\ProcessData.java

1.2.3. Execute code with the command

java edu.gmu.NOVEC.preprocess.ProcessData [root directory]

1.3. Results/Post-Execution steps: A file for each BRC will be produced in the same folder as the

execution folder with the naming convention [BRC][CustomerId]_SurveyData.csv. The user

should move these files to a common folder for further processing with individual subfolders

for each BRC code. These files are a full data set for an individual customer with columns for

time-span filtering as described in Table 4 Merged File Column Headings above.

2. BoundCustomers.java



- Sub Folders named for each BRC code

- Output from ProcessData described above with files named with the convention

[BRC][CustomerId]_SurveyData.csv as described above.

- Presence of a CENSUS_DATA.csv file in each BRC subfolder





edu\gmu\NOVEC\preprocess\BoundCustomers.java


java edu.gmu.NOVEC.preprocess.BoundCustomers [root]\[BRC] [BRC]

recommend that user creates a batch file containing the command repeated for each of the

BRCs

2.3. Results/Post-Execution steps: A file for each BRC will be produced in the same folder as each

BRC source file with the naming convention [BRC][CustomerId]_SurveyNormalized.csv.

These files are a full data set for an individual customer with columns for time-span filtering

as described in Table 5 Merged Customer File Format above with the additional columns

giving the Normalized against CSA and Normalized against CSM scaled Watt usage.


27

3. BoundDailies.java




- Output from ProcessData described above with files named with the convention

[BRC][CustomerId]_ DailyTotals.csv as described above.






edu\gmu\NOVEC\preprocess\BoundDailies.java


java edu.gmu.NOVEC.preprocess.BoundDailies [root]\[BRC] [BRC]


BRCs


BRC source file with the naming convention [BRC][CustomerId]_DailyNormalized.csv.

These files are a full data set for an individual customer with columns for time-span filtering

as described in Table 5 Merged Customer File Format above with the additional columns

giving the Normalized against CSA and Normalized against CSM scaled Watt usage

4. MergeCustomersNorm.java

4.1. Requirements:

- Folder organized with the following structure and naming convention



- Output from BoundCustomers described above with files named with the convention

[BRC][CustomerId]_SurveyNormalized.csv as described above.


- Presence of a full-1300-day continuous time span dummy data file “000Customer

00000DateRange_SurveyNormalized.csv”





edu\gmu\NOVEC\preprocess\MergeCustomersNorm.java


java edu.gmu.NOVEC.preprocess.MergeCustomersNorm [root]\[BRC] [BRC]

[MERGEVAL]


BRCs where MERGEVAL is replaced with RAW, NORMAVG, or NORMMAX

depending on if the user wants to raw wattage or CSA-normalized or CSM-normalized data


BRC source file with the naming convention [BRC]_[MERGEVAL]_[Date of first data


28

point].csv. These files are a full data set for a BRC-grouped set of customers with columns for

time-span filtering as described in Table 5 Merged Customer File Format above with the

additional columns giving the Typical value used for HDFT metrics described in the report.

The output of these files can be copied and pasted into the Hourly Spinner graphing

visualization tools described in Appendix C

5. MergeCustomersDailyNorm.java

5.1. Requirements:




- Output from BoundDailies described above with files named with the convention

[BRC][CustomerId]_DailyNormalized.csv as described above.


- Presence of a full-1300-day continuous time span dummy data file

“000Customer000_DailyNormalized.csv”





edu\gmu\NOVEC\preprocess\MergeCustomersDailyNorm.java


java edu.gmu.NOVEC.preprocess. MergeCustomersDailyNorm [root]\[BRC] [BRC]

[MERGEVAL]





BRC source file with the naming convention [BRC]DailyMerged_[MERGEVAL]_[Date of

first data point].csv. These files are a full daily data set for a BRC-grouped set of customers

with columns for time-span filtering as described in Table 5 Merged Customer File Format

above with the additional columns giving the Typical value used for HDFT metrics described in

the report. The output of these files can be copied and pasted into the Daily Spinner graphing


6. FilteredLoadFactorCrawler.java outputs the main Excel File necessary for the end product and

post-processing metrics.

6.1. Requirements:




- Output from MergeCustomersNorm described above with files named with the convention

[BRC]_[MERGEVAL]_[Date of first data point].csv as described above.




29



edu\gmu\NOVEC\model\FilteredLoadFactor.java

edu\gmu\NOVEC\preprocess\FilteredLoadFactorCrawler.java


java edu.gmu.NOVEC.preprocess. FilteredLoadFactorCrawler [root] [MERGEVAL]

where the recommended value for MERGEVAL is RAW

6.3. Results/Post-Execution steps: A file containing all the necessary FLB values across different

time scales to do threshold tuning as described in Appendix C regarding the FLB Processing

File

7. ReclassifyNorm.java Similar to combining steps of ProcessData.java and

BoundCustomers/BoundDailies steps above but puts single-customer files (renamed with class

prefixes) into individual SurveyNormalized and DailyNormalized files grouped into Class [##]

subfolders

7.1. Requirements:




- Output from BoundCustomers/BoundDailies described above with files named with the

convention [BRC][CustomerId]_SurveyNormalized.csv and

[BRC][CustomerId]_DailyNormalized.csv as described above.

- A classification assignment file named ReClass24.csv as populated by operation of the FLB

Evaluation excel spreadsheet described in Appendix C




javac edu\gmu\NOVEC\model\NOVECCustomer.java edu\gmu\NOVEC\preprocess\

ReclassifyNorm.java


java edu.gmu.NOVEC.preprocess. ReclassifyNorm [root] [any]

7.3. Results/Post-Execution steps: Each customer file will be reassigned a “Class [##]” prefix to

replace the BRC prefix and automatically copied to a “Class [##]” subfolder

8. MergeClassNorm.java

8.1. Requirements:



- Sub Folders named for each Class

- Output from ReclassifyNorm described above with files named with the convention [Class

##][CustomerId]_SurveyNormalized.csv as described above.

- Presence of a ReClass24.csv file in each Class subfolder

- Presence of a full-1300-day continuous time span dummy data file “000Customer

00000DateRange_SurveyNormalized.csv”




30



edu\gmu\NOVEC\preprocess\MergeClassNorm.java


java edu.gmu.NOVEC.preprocess. MergeClassNorm [root]\[Class] [Class] [MERGEVAL]




8.3. Results/Post-Execution steps: A file for each Class will be produced in the same folder as

each BRC source file with the naming convention [Class]_[MERGEVAL]_[Date of first data

point].csv. These files are a full data set for a Class -grouped set of customers with columns

for time-span filtering as described in Table 5 Merged Customer File Format above with the

additional columns giving the Typical value used for HDFT metrics described in the report.

The output of these files can be copied and pasted into the Hourly Spinner graphing


9. MergeClassDailyNorm similar to MergeCustomersDailyNorm above. Copy/paste and I will help

update

9.1. Requirements:



- Sub Folders named for each Class code

- Output from ReclassDailyNorm described above with files named with the convention

[Class][CustomerId]_DailyNormalized.csv as described above.

- Presence of a ReClass24.csv file in each Class subfolder

- Presence of a full-1300-day continuous time span dummy data file

“000Customer000_DailyNormalized.csv”





edu\gmu\NOVEC\preprocess\MergeClassDailyNorm.java


java edu.gmu.NOVEC.preprocess. MergeClassDailyNorm [root]\[Class] [Class]

[MERGEVAL]




9.3. Results/Post-Execution steps: A file for each Class will be produced in the same folder as

each BRC source file with the naming convention [Class]DailyMerged_[MERGEVAL]_[Date

of first data point].csv. These files are a full daily data set for a Class -grouped set of

customers with columns for time-span filtering as described in Table 5 Merged Customer File

Format above with the additional columns giving the Typical value used for HDFT metrics

described in the report. The output of these files can be copied and pasted into the Daily

Spinner graphing visualization tools described in Appendix C


31

10. HourlyDepartureFromTypicalClass.java

10.1. Requirements:



- Sub Folders named for each Class code

- Output from MergeClassNorm described above with files named with the convention

[Class]_[MERGEVAL]_[Date of first data point].csv as described above.




javac edu\gmu\NOVEC\model\NOVECCustomer.java edu\gmu\NOVEC\preprocess\

HourlyDepartureFromTypicalClass.java


java edu.gmu.NOVEC.preprocess.HourlyDepartureFromTypicalClass [root]

[MERGEVAL]

where MERGEVAL is recommended to be replaced with NORMAVG

10.3. Results/Post-Execution steps: Multiple intermediate files of the following naming convention

and description

- [Class ##]_HSDFT_[MERGEVAL]_[Date of first data point].csv the hourly departure for

each customer, by time index scaled to the standard of deviation for that time-specific typical

mean.

- [Class ##]_HSDFT_Count[MERGEVAL]_[Date of first data point].csv the zero/one values

for each customer indicating whether that data point is inside or outside of two standard of

deviations from mean

- [Class ##]_HSDFT_JustCounts[MERGEVAL]_[Date of first data point].csv the sum of

zero/one values from the file above for each class.

The last set of files can be used to create rankings of the customers within a group to assess the

similarity of customers within that group.

A similar file exists within the project called HourlyDepartureFromTypical.java that is geared

towards BRC-merged files with slightly different column arrangements, but is not necessary

beyond re-verification of BRC assessment as having dissimilar customers.


32

7 APPENDIX C: Excel Process File Assembly and Operation The following figures depict the Excel spreadsheets used to create a quickly scrollable view of the

hourly and daily consumption data points for different groupings of either BRC or Class. Detailed

instructions on how to update the files are included below the figures.

Each file is supported by two worksheets consisting of a data set tab, and a graphing/scrolling tab. To

update or create a file, perform the following steps.

1. Take a BRC- or Class-merged file output from steps 4, 5, 8 or 9 in Appendix B above and paste

the data into the first worksheet, updating the label of the worksheet to match the data set

2. Update the Worksheet label cell in B to assure proper values are passed to the Address() function

calls in C

3. Verify that the appropriate values are being passed to the Indirect() function calls in D when

updating the customer and time span spinners in A.


33

The screen capture above shows how the Excel FLB processing file gives the analyst the ability to set

the upper and lower threshold values in Callout A to qualify a Customer as meeting conditional logic in

columns at B to qualify as meeting one of the labeled permutations of the seasonal, day/night or day of

week variables. These Boolean values are translated into text values in hidden columns at C and Excel

Match() and Index() functions in D are used to match lookup values in the master list of classes at F. The

list of classes also provides a dynamically updated count of the number of customer members in each of

the 24 bins. The full Excel files can be downloaded from the GMU website at

http://seor.gmu.edu/projects/SEOR-Fall14/NOVEC/deliver.html.

The Filtered Load Burden file that maps metrics to class assignments can be created or updated with

the following steps.

1. Open the file produced in step 6 of Appendix B above and paste the data into an existing FLB

evaluation file (included in GMU-hosted files)

2. Update the threshold values located immediately to the right of the final FLB output column

3. Ensure that the header labels in callout B match your currently defined set of class label inputs for

each of the major time-scales (seasonal, weekly, day/night)

4. Evaluate the final classification matching fed by the Index() and Match() functions processed at D

and F



34

8 APPENDIX D: Class Assignments and Output Artifacts

Raw data files and Excel FLB processing files can be found hosted on GMU website at

http://seor.gmu.edu/projects/SEOR-Fall14/NOVEC/deliver.html including Data File descriptions.

Original BRC Census Data Table to provide context

BRC Description Count

ES1 All Electric School 23

FL1 Athletic Field Lighting 23

IS Interruptible 8

IS1 Interruptible 1

LP Large Power 244

LP1 Large Power 88

LP2 Large Power 1

LP5 Large Power 17

1A1 Residential 4

RES Residential 461

RTO Residential - Time of Use 19

SC Small Commercial 1 Phase 85






GMU Special Customer 1

HVA Special Customer 1

UNK UNKNOWN 39

ID BRC Class MW Summer FLB

Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0001 ES1 16.1 2,590 0.88 0.99 1.12 1.83 0.55 2.16 0.46

0002 LP1 13 1,006 0.84 1.40 0.85 1.33 0.75 1.32 0.76

0003 RES 09 97 0.72 2.25 0.55 1.00 1.00 0.97 1.03

0004 RES 06 50 0.79 1.75 0.68 0.90 1.11 1.08 0.93

0005 RES 01 30 1.66 0.70 0.84 0.94 1.06 1.35 0.74

0006 SC4 #N/A 0 1.07 0.59 1.18 NaN NaN NaN NaN

0007 LP1 18 254 1.11 1.11 0.83 1.23 0.82 1.09 0.92

0008 LP 09 2,640 1.15 1.16 0.77 1.01 0.99 0.91 1.09

0009 SC 06 33 0.52 2.75 0.56 0.97 1.03 0.97 1.04



35


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0010 SC1 05 113 0.54 2.20 0.69 1.04 0.97 0.71 1.41

0011 SC1 01 63 3.37 0.39 0.48 0.92 1.09 1.59 0.63

0012 SC1 06 28 0.71 1.96 0.64 1.01 0.99 1.07 0.94

0013 SC1 06 130 0.05 4.61 0.59 1.01 0.99 1.03 0.97

0014 UNK 18 46 0.92 1.54 0.69 1.16 0.86 1.05 0.95

0015 SC4 9.1 229 0.93 1.12 0.96 0.99 1.01 1.00 1.00

0016 LP 7.1 5,645 1.02 0.99 0.99 1.13 0.88 1.11 0.90

0017 SC5 7.1 104 1.06 0.92 1.03 0.98 1.02 1.26 0.79

0018 RES 10 4,938 1.29 0.78 0.98 1.18 0.85 1.46 0.68

0019 SC4 10 28 1.36 0.92 0.81 1.39 0.72 2.21 0.45

0020 SC4 01 196 1.41 0.70 0.98 1.13 0.89 3.27 0.31

0021 LP 10 256 1.75 0.61 0.87 0.83 1.20 2.85 0.35

0022 SC4 7.1 150 1.15 0.76 1.11 1.06 0.94 2.16 0.46

0023 SC4 10 94 1.17 0.90 0.96 1.49 0.67 3.52 0.28

0024 RES 07 70 1.04 1.54 0.61 0.99 1.01 1.32 0.76

0025 SC4 16.1 15 1.10 0.92 1.00 3.12 0.32 4.43 0.23

0026 LP 13 3,194 0.82 1.36 0.88 1.27 0.79 1.15 0.87

0027 SC4 16.1 87 1.03 0.88 1.09 2.60 0.38 4.38 0.23

0028 RES 01 124 1.59 0.82 0.79 1.06 0.94 1.69 0.59

0029 LP 16.1 2,258 1.15 0.88 0.99 1.17 0.85 1.19 0.84

0030 SC4 10 47 1.95 0.67 0.75 1.87 0.53 2.32 0.43

0031 LP 13 1,872 0.89 1.28 0.88 1.60 0.62 2.04 0.49

0032 LP 04 2,485 0.95 1.15 0.92 0.92 1.08 1.13 0.89

0033 SC4 05 194 0.68 1.49 0.93 0.99 1.01 0.26 3.89

0034 LP 8.1 1,159 0.73 1.21 1.09 0.99 1.01 0.85 1.18

0035 RES 09 439 0.78 1.93 0.61 0.94 1.06 0.95 1.05

0036 SC4 16 130 1.11 1.17 0.78 2.04 0.49 2.15 0.46

0037 LP1 16.1 3,671 1.05 0.83 1.12 2.18 0.46 2.14 0.47

0038 RES 07 29 1.96 0.79 0.64 0.97 1.03 1.32 0.76

0039 LP 01 1,273 1.68 0.62 0.89 1.05 0.95 1.73 0.58

0040 LP 01 8,045 1.20 0.92 0.91 0.95 1.06 1.31 0.76

0041 LP 10 242 1.60 0.76 0.83 1.73 0.58 2.10 0.48

0042 SC4 10 62 1.82 0.69 0.78 2.01 0.50 2.12 0.47

0043 RES 15 57 0.79 1.79 0.67 0.87 1.15 0.94 1.06

0044 RES 07 52 1.68 0.90 0.65 0.90 1.11 1.37 0.73

0045 RES 07 66 0.97 1.38 0.75 0.97 1.03 1.17 0.85

0046 RES 03 16 1.68 0.76 0.79 1.02 0.98 1.09 0.92

0047 RES 16 5 1.46 1.37 0.47 0.36 2.75 2.89 0.35

0048 LP 10 1,475 1.24 0.88 0.91 3.41 0.29 5.58 0.18

0049 LP 13 576 0.89 1.18 0.95 1.26 0.79 1.19 0.84


36


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0050 LP 01 929 1.39 0.75 0.94 0.98 1.02 1.36 0.74

0051 LP 03 604 1.11 1.05 0.88 1.12 0.90 1.08 0.93

0052 SC4 10 180 1.25 0.95 0.86 1.88 0.53 2.03 0.49

0053 LP 13 1,629 0.69 2.02 0.67 1.16 0.86 1.52 0.66

0054 RES 01 36 1.57 0.84 0.78 0.94 1.06 1.39 0.72

0055 UNK 06 62 0.68 2.01 0.61 1.02 0.98 0.91 1.09

0056 SC4 01 37 1.80 0.67 0.84 1.09 0.92 1.27 0.78

0057 SC 05 29 0.85 1.19 0.97 1.00 1.00 0.51 1.94

0058 SC 05 13 1.00 1.14 0.87 0.99 1.01 0.48 2.07

0059 SC4 01 267 1.27 0.92 0.88 0.98 1.02 1.48 0.67

0060 RES 07 55 0.97 1.69 0.59 0.92 1.08 1.15 0.87

0061 SC4 01 204 1.30 0.78 1.01 1.02 0.98 1.22 0.82

0062 SC 9.1 78 1.19 0.85 1.01 1.01 0.99 1.03 0.97

0063 SC 9.1 242 1.10 0.93 0.99 1.01 0.99 1.08 0.93

0064 LP 03 29,501 1.19 0.89 0.96 1.08 0.93 1.08 0.93

0065 LP 06 3,369 0.93 1.32 0.81 0.99 1.02 0.97 1.03

0066 LP 13 4,421 0.84 1.32 0.90 1.16 0.86 1.13 0.89

0067 RES 07 29 1.67 1.13 0.51 0.93 1.07 1.11 0.90

0068 LP 16.1 242 1.03 1.07 0.92 2.29 0.44 2.40 0.42

0069 IS 12 18,614 1.33 0.76 0.96 1.33 0.75 0.98 1.02

0070 LP1 16.1 3,531 1.18 0.73 1.12 1.97 0.51 2.47 0.40

0071 RES 04 50 0.79 1.74 0.68 1.03 0.97 1.26 0.79

0072 LP 16.1 3,499 1.08 1.02 0.92 1.54 0.65 1.75 0.57

0073 LP 01 339 1.36 0.72 0.99 1.13 0.88 3.29 0.30

0074 SC 04 76 0.82 1.44 0.82 1.14 0.88 1.29 0.77

0075 RES 12 9 2.29 0.66 0.63 1.23 0.82 1.09 0.92

0076 SC 16 15 1.59 1.11 0.57 1.20 0.83 1.43 0.70

0077 RES 07 30 1.35 0.99 0.77 1.02 0.98 1.31 0.76

0078 LP 7.1 4,183 1.02 1.09 0.91 1.07 0.93 1.39 0.72

0079 RES 04 65 0.96 1.14 0.91 0.93 1.07 1.14 0.88

0080 RES 04 35 0.99 1.21 0.84 0.90 1.11 1.18 0.85

0081 SC4 05 237 0.71 1.43 0.91 0.98 1.02 0.21 4.72

0082 SC4 05 161 0.79 1.28 0.95 1.00 1.00 0.25 3.97

0083 RES 07 15 1.64 0.90 0.67 1.01 0.99 1.89 0.53

0084 UNK 01 15 2.24 0.57 0.75 0.95 1.05 1.43 0.70

0085 RES 07 74 1.16 1.32 0.67 0.97 1.03 1.31 0.77

0086 LP 01 570 1.36 0.78 0.93 1.06 0.95 1.33 0.75

0087 RES 09 23 1.10 1.15 0.81 0.88 1.14 0.97 1.03

0088 LP1 07 305 0.98 1.34 0.77 1.11 0.90 1.37 0.73

0089 RES 07 67 1.76 0.84 0.65 0.96 1.04 1.20 0.83


37


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0090 SC 10 286 1.18 0.96 0.90 1.52 0.66 1.23 0.81

0091 RES 04 41 0.57 2.20 0.66 0.99 1.01 1.11 0.90

0092 RES 01 77 1.70 0.73 0.81 0.94 1.07 1.11 0.90

0093 RES 16 25 1.32 0.97 0.80 0.85 1.18 1.60 0.63

0094 SC 10 94 1.62 0.69 0.86 1.83 0.55 2.05 0.49

0095 RES 04 65 0.67 1.95 0.68 1.01 0.99 1.26 0.79

0096 RES 13 28 0.51 3.14 0.46 0.65 1.54 1.44 0.69

0097 RES 17 19 1.69 0.94 0.62 0.69 1.45 0.84 1.20

0098 RES 07 72 1.05 1.58 0.59 0.92 1.09 1.11 0.90

0099 LP 07 1,857 1.05 1.18 0.82 1.01 0.99 1.17 0.86

0100 LP 06 328 0.82 1.53 0.78 0.97 1.03 1.04 0.96

0101 RES 10 33 2.52 0.48 0.72 0.86 1.16 1.52 0.66

0102 RES 10 26 1.39 0.94 0.79 0.80 1.25 1.34 0.74

0103 RES 04 56 0.76 1.70 0.75 0.94 1.07 1.15 0.87

0104 LP 01 6,914 1.29 0.72 1.05 1.03 0.97 1.23 0.81

0105 SC4 N/A 0 0.70 1.43 0.79 1.18 0.85 NaN NaN

0106 RES 01 46 1.40 0.88 0.81 0.88 1.14 1.31 0.76

0107 RES 07 41 0.76 2.26 0.52 0.92 1.09 1.18 0.85

0108 RES 06 68 0.79 1.49 0.81 0.92 1.08 1.00 1.00

0109 RES 01 41 1.67 0.79 0.77 1.06 0.95 1.51 0.66

0110 LP 16.1 396 0.61 1.34 1.12 1.43 0.70 1.41 0.71

0111 LP 11 49 1.11 1.02 0.90 1.27 0.79 0.84 1.19

0112 RES 01 38 1.95 0.70 0.68 0.93 1.07 1.78 0.56

0113 RES 01 46 1.71 0.80 0.74 0.91 1.10 1.16 0.86

0114 RES 04 90 0.81 1.49 0.81 0.94 1.06 1.54 0.65

0115 LP 01 1,057 2.11 0.70 0.62 0.89 1.12 1.48 0.67

0116 RES 06 49 0.27 3.53 0.57 0.97 1.03 1.00 1.00

0117 RES 01 87 1.25 0.94 0.85 1.01 0.99 1.63 0.61

0118 LP1 16.1 4,469 1.15 0.77 1.10 2.23 0.45 1.90 0.53

0119 LP1 16.1 8,972 1.01 0.85 1.14 1.40 0.71 1.34 0.75

0120 LP5 01 610 2.05 0.55 0.83 1.03 0.97 1.15 0.87

0121 UNK 01 23 2.76 0.50 0.63 0.92 1.08 1.36 0.74

0122 IS 10 4,063 1.23 0.78 1.01 1.72 0.58 1.14 0.88

0123 RES 07 67 1.54 0.89 0.72 0.94 1.07 1.29 0.77

0124 LP 01 347 1.40 0.79 0.92 1.03 0.97 1.22 0.82

0125 SC4 01 122 1.96 0.65 0.74 1.07 0.93 1.73 0.58

0126 ES1 13 5,372 0.71 1.41 0.93 1.26 0.80 1.33 0.75

0127 RES 01 12 1.64 0.73 0.84 1.01 0.99 1.30 0.77

0128 LP1 7.1 6,288 1.00 0.98 1.02 0.95 1.05 1.14 0.88

0129 LP 16.1 889 1.10 0.97 0.93 2.99 0.33 1.88 0.53


38


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0130 RES 09 51 1.23 1.05 0.79 0.99 1.01 1.09 0.92

0131 UNK 08 31 1.11 1.16 0.79 0.99 1.01 0.76 1.31

0132 SC 9.1 5 1.09 0.90 1.00 1.01 0.99 1.00 1.00

0133 SC4 16.1 16 1.03 1.03 0.95 4.83 0.21 6.41 0.16

0134 SC4 05 240 0.72 1.42 0.91 1.00 1.00 0.15 6.70

0135 RES 14 175 0.37 3.10 0.57 0.79 1.26 0.85 1.18

0136 LP1 16.1 4,595 1.11 0.76 1.14 1.47 0.68 1.48 0.68

0137 SC 05 5 0.89 1.13 0.98 1.03 0.97 0.48 2.07

0138 SC6 16 113 0.91 1.53 0.70 1.66 0.60 1.72 0.58

0139 SC 05 12 0.83 1.23 0.95 1.00 1.00 0.42 2.35

0140 SC 9.1 44 1.05 1.00 0.96 1.01 0.99 1.07 0.93

0141 SC 05 3 0.86 1.18 0.96 1.00 1.00 0.50 1.99

0142 SC 05 7 0.90 1.25 0.88 0.99 1.01 0.40 2.48

0143 LP 05 288 0.94 1.05 1.00 0.99 1.01 0.68 1.46

0144 LP 15 481 0.52 2.10 0.79 1.17 0.85 1.01 0.99

0145 IS 16 1,821 1.34 1.01 0.75 6.09 0.16 8.07 0.12

0146 RES 7.1 51 1.11 0.94 0.96 1.04 0.97 1.37 0.73

0147 RES 01 13 1.11 1.00 0.92 0.98 1.02 1.43 0.70

0148 SC2 9.1 3 0.95 1.04 1.01 1.00 1.00 1.00 1.00

0149 SC N/A 0 NaN 1.00 1.00 1.00 1.00 1.00 1.00

0150 UNK 9.1 23 0.98 1.03 0.99 1.00 1.00 1.00 1.00

0151 SC 9.1 9 1.01 1.00 1.00 1.00 1.00 1.00 1.00

0152 SC 9.1 24 1.03 0.98 0.99 1.00 1.00 1.00 1.00

0153 RT0 09 8 1.24 1.21 0.71 0.90 1.11 1.06 0.95

0154 RES 07 42 0.94 1.52 0.69 0.98 1.02 1.19 0.84

0155 RES 06 57 0.85 1.48 0.77 1.01 0.99 1.08 0.93

0156 RES 01 28 2.03 0.64 0.76 0.90 1.11 1.70 0.59

0157 RES 07 42 0.82 1.93 0.59 0.91 1.09 1.38 0.73

0158 LP5 06 401 0.61 1.99 0.72 1.08 0.93 1.07 0.94

0159 RT0 06 18 0.81 1.74 0.68 0.90 1.11 1.09 0.92

0160 SC 16 36 1.27 1.29 0.63 1.56 0.64 1.84 0.54

0161 SC4 05 243 0.93 1.10 0.96 0.98 1.02 0.64 1.56

0162 SC4 16 113 1.04 1.27 0.77 1.46 0.69 2.97 0.34

0163 RES 03 63 1.84 0.81 0.69 0.91 1.10 0.99 1.01

0164 SC4 01 79 1.55 0.68 0.96 0.99 1.01 2.08 0.48

0165 SC4 13 52 0.89 1.39 0.79 1.27 0.79 1.95 0.51

0166 SC4 01 91 1.30 0.82 0.97 1.12 0.89 3.13 0.32

0167 RES 01 78 1.38 0.95 0.80 0.99 1.01 1.19 0.84

0168 RES 01 37 1.67 0.77 0.81 0.99 1.01 1.37 0.73

0169 SC4 16.1 25 0.64 1.03 1.31 1.40 0.72 2.90 0.34


39


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0170 RES 06 237 0.67 1.70 0.78 1.06 0.95 1.04 0.97

0171 HVA 9.1 410,087 1.06 0.94 1.00 1.01 0.99 1.01 0.99

0172 1A1 10 785 2.09 0.76 0.64 0.78 1.28 1.17 0.86

0173 LP 03 2,034 2.08 0.54 0.84 0.92 1.09 1.02 0.98

0174 SC 7.1 424 1.16 0.85 1.03 1.01 0.99 1.11 0.90

0175 LP 01 1,047 1.50 0.89 0.75 0.98 1.02 1.57 0.64

0176 RES 01 2,298 1.28 0.91 0.89 0.99 1.01 1.30 0.77

0177 FL1 17.1 161 1.11 0.58 1.42 1.41 0.71 0.47 2.12

0178 SC 05 134 0.96 1.17 0.90 1.00 1.00 0.84 1.19

0179 SC 05 17 0.86 1.21 0.94 0.97 1.03 0.47 2.11

0180 SC 05 6 0.85 1.17 0.98 0.99 1.01 0.38 2.60

0181 LP1 16.1 2,869 1.10 0.89 1.03 2.45 0.41 2.13 0.47

0182 LP1 16.1 10,457 1.12 0.88 1.02 1.44 0.69 1.91 0.52

0183 RES 10 38 2.02 0.63 0.74 0.86 1.16 1.85 0.54

0184 RES 17 34 1.88 0.93 0.58 0.84 1.19 0.60 1.66

0185 LP 01 263 1.47 0.72 0.96 1.12 0.90 2.30 0.44

0186 RES 04 40 0.77 1.71 0.71 0.94 1.06 1.21 0.83

0187 LP 16.1 985 1.44 0.53 1.22 2.66 0.38 4.53 0.22

0188 LP 10 213 1.40 0.82 0.90 1.53 0.65 1.65 0.61

0189 RES 03 43 1.72 0.67 0.86 1.03 0.98 1.10 0.91

0190 RES 03 48 2.24 0.68 0.65 0.93 1.08 1.03 0.97

0191 SC 16 37 1.02 1.20 0.83 1.25 0.80 1.30 0.77

0192 RES 07 47 1.44 1.08 0.67 0.98 1.02 1.33 0.75

0193 RES 10 433 1.55 0.78 0.84 0.80 1.24 1.41 0.71

0194 RES 10 303 2.15 0.67 0.70 0.72 1.40 1.15 0.87

0195 RES 10 174 2.28 0.63 0.69 0.70 1.43 1.38 0.73

0196 RES 01 50 2.13 0.70 0.67 0.92 1.09 1.86 0.54

0197 LP5 10 4,495 1.28 0.91 0.89 1.23 0.81 1.85 0.54

0198 SC4 01 195 1.24 0.84 0.98 0.99 1.01 1.20 0.84

0199 RT0 01 20 2.24 0.58 0.68 0.97 1.04 1.57 0.64

0200 LP 10 5,711 1.09 1.03 0.90 1.57 0.64 1.87 0.54

0201 LP 16.1 9,953 1.07 1.06 0.90 1.57 0.64 1.74 0.57

0202 LP 16.1 10,950 1.04 1.02 0.95 1.64 0.61 1.78 0.56

0203 RES 03 108 1.77 0.81 0.73 0.97 1.03 1.08 0.93

0204 RES 07 86 1.36 1.11 0.68 1.07 0.93 1.18 0.85

0205 RES 16 323 0.95 1.35 0.78 0.85 1.18 1.77 0.56

0206 SC 01 557 1.23 0.82 1.00 0.97 1.03 1.33 0.75

0207 SC5 16.1 371 1.09 0.83 1.10 1.62 0.62 1.41 0.71

0208 LP5 16.1 3,791 0.95 1.05 0.99 1.96 0.51 1.83 0.55

0209 RES 01 21 1.97 0.59 0.84 0.89 1.13 1.61 0.62


40


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0210 RES 10 13 1.43 0.91 0.80 0.73 1.38 1.22 0.82

0211 SC 9.1 15 1.00 0.99 1.01 1.00 1.00 1.00 1.00

0212 RES 01 42 1.48 0.89 0.79 1.00 1.00 1.69 0.59

0213 RES 07 94 1.04 1.33 0.73 0.95 1.05 1.12 0.89

0214 RES 01 37 1.96 0.67 0.75 1.03 0.97 1.88 0.53

0215 RES 07 85 0.87 1.54 0.72 0.99 1.01 1.20 0.83

0216 SC 9.1 10 1.02 0.98 1.00 1.00 1.00 1.00 1.00

0217 SC 9.1 8 1.01 0.99 1.01 1.00 1.00 1.00 1.00

0218 SC 9.1 14 1.00 0.99 1.01 1.00 1.00 1.00 1.00

0219 SC6 04 177 0.82 1.25 0.94 0.96 1.04 1.19 0.84

0220 LP 05 268 1.01 0.94 1.06 0.99 1.01 0.60 1.65

0221 SC 9.1 15 0.99 1.02 0.99 1.00 1.00 1.00 1.00

0222 LP 03 1,917 1.32 0.92 0.86 1.00 1.00 0.96 1.04

0223 LP 03 1,303 1.21 0.89 0.94 1.02 0.98 0.99 1.01

0224 LP 13 5,187 0.98 1.11 0.92 1.85 0.54 2.01 0.50

0225 LP 13 4,426 0.87 1.25 0.90 1.91 0.52 2.61 0.38

0226 RES 05 28 0.74 1.67 0.81 0.99 1.01 0.81 1.23

0227 RES 04 67 0.81 1.61 0.72 0.90 1.11 1.12 0.89

0228 RES 04 41 0.45 2.09 0.82 0.97 1.03 1.11 0.90

0229 LP 16.1 61,171 1.04 1.00 0.97 1.20 0.83 1.22 0.82

0230 RES 07 28 1.49 1.03 0.63 0.92 1.09 1.39 0.72

0231 LP 9.1 6,254 0.99 0.92 1.09 1.02 0.98 1.02 0.98

0232 RES 07 39 1.34 1.09 0.71 0.99 1.01 1.26 0.79

0233 RT0 03 14 1.88 0.60 0.72 1.00 1.00 1.04 0.96

0234 RES 01 1,107 1.35 0.89 0.86 0.91 1.10 1.65 0.61

0235 RES 01 17 2.35 0.65 0.65 0.96 1.04 1.43 0.70

0236 SC4 01 302 1.55 0.65 0.98 1.08 0.92 1.85 0.54

0237 LP 01 13,977 1.26 0.86 0.95 1.08 0.93 1.64 0.61

0238 SC4 04 171 0.92 1.14 0.94 1.08 0.93 1.15 0.87

0239 RES 06 68 0.87 1.43 0.79 1.03 0.97 1.03 0.97

0240 RT0 06 30 0.49 2.16 0.85 1.05 0.95 0.98 1.02

0241 RES 03 34 1.66 0.81 0.76 0.92 1.08 1.02 0.99

0242 LP 03 1,865 1.31 0.89 0.89 1.00 1.00 0.98 1.02

0243 UNK 7.1 56 1.07 0.90 1.06 1.11 0.90 2.49 0.40

0244 UNK 9.1 15 1.10 1.01 0.93 1.03 0.98 0.99 1.01

0245 LP 04 156 0.75 1.42 0.88 0.92 1.08 1.78 0.56

0246 SC 9.1 35 1.09 0.92 1.01 1.01 0.99 1.01 0.99

0247 SC 13 27 1.00 1.17 0.86 1.20 0.84 1.72 0.58

0248 RES 04 29 0.52 2.16 0.69 1.05 0.95 1.16 0.86

0249 RES 01 52 1.86 0.71 0.77 0.94 1.06 1.44 0.69


41


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0250 RES 07 52 1.05 1.23 0.78 0.93 1.07 1.24 0.81

0251 LP1 16.1 2,424 1.01 0.95 1.04 2.51 0.40 2.36 0.42

0252 RES 09 29 1.55 0.97 0.70 0.94 1.07 1.10 0.91

0253 RES 04 63 0.60 1.67 0.85 0.96 1.05 1.21 0.82

0254 LP 16.1 448 1.04 1.04 0.94 1.22 0.82 1.23 0.81

0255 SC 13 151 0.69 1.92 0.67 1.47 0.68 1.19 0.84

0256 RES 01 41 2.30 0.72 0.60 0.93 1.07 1.11 0.90

0257 RES 07 51 1.02 1.34 0.73 1.04 0.96 1.91 0.52

0258 RES 01 52 1.68 0.76 0.81 0.91 1.09 1.16 0.86

0259 RES 04 63 0.77 1.76 0.68 0.93 1.07 1.16 0.86

0260 RES 01 25 1.73 0.72 0.82 0.97 1.03 1.26 0.79

0261 LP 13 406 0.86 1.52 0.75 1.33 0.75 1.32 0.76

0262 UNK 9.1 13 1.00 1.00 1.00 1.00 1.00 1.00 1.00

0263 UNK 9.1 10 1.00 1.00 1.00 1.00 1.00 1.00 1.00

0264 RT0 01 18 1.55 0.87 0.73 0.99 1.01 1.40 0.71

0265 RES 10 520 1.63 0.76 0.82 1.19 0.84 1.51 0.66

0266 SC4 04 77 0.78 1.21 1.01 0.95 1.05 1.19 0.84

0267 LP 9.1 22,653 0.97 1.07 0.96 1.00 1.00 1.03 0.97

0268 LP 9.1 19,781 1.06 0.99 0.96 0.98 1.02 1.02 0.98

0269 LP 03 535 1.95 0.61 0.81 0.94 1.07 1.07 0.93

0270 RES 15 71 0.59 2.27 0.62 0.86 1.16 0.95 1.05

0271 RES 01 40 1.91 0.74 0.67 1.04 0.96 1.23 0.81

0272 RES 06 45 0.45 2.73 0.59 0.99 1.01 1.09 0.92

0273 RES 01 31 1.43 0.66 1.01 1.07 0.94 1.26 0.79

0274 RES 01 55 1.26 0.97 0.85 0.99 1.01 1.43 0.70

0275 RES 01 46 1.62 0.72 0.84 0.93 1.07 1.21 0.83

0276 RES 07 59 1.30 1.17 0.67 0.99 1.01 1.13 0.88

0277 RES 07 57 1.28 1.30 0.61 0.91 1.10 1.17 0.85

0278 LP 08 619 1.17 1.10 0.79 1.01 0.99 0.82 1.22

0279 RES 08 54 0.89 1.86 0.57 0.97 1.03 0.89 1.12

0280 RES 07 97 1.59 0.89 0.69 0.98 1.02 1.61 0.62

0281 RES 07 42 1.25 1.20 0.69 0.95 1.06 1.12 0.90

0282 RT0 04 31 0.92 1.37 0.81 0.97 1.04 1.12 0.89

0283 RES 01 28 1.98 0.63 0.77 0.99 1.01 1.33 0.75

0284 LP 7.1 673 1.07 1.03 0.92 1.11 0.90 3.17 0.31

0285 RES 01 28 2.80 0.45 0.67 0.99 1.01 1.56 0.64

0286 RES 01 32 1.90 0.74 0.72 0.89 1.12 1.27 0.79

0287 RES 07 47 0.99 1.31 0.78 0.98 1.02 1.23 0.81

0288 LP5 01 4,646 1.17 1.01 0.85 1.05 0.96 1.32 0.76

0289 LP 07 779 1.17 1.11 0.78 1.03 0.97 1.14 0.88


42


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0290 RES 08 90 0.98 1.38 0.74 0.95 1.06 0.83 1.21

0291 RES 07 67 0.93 1.41 0.75 0.98 1.02 1.29 0.78

0292 SC 10 111 1.24 0.97 0.85 1.95 0.51 2.14 0.47

0293 LP 9.1 2,632 1.07 0.82 1.09 1.04 0.96 0.95 1.06

0294 SC4 01 313 1.45 0.67 1.01 0.97 1.03 1.20 0.84

0295 RES 07 75 1.53 1.01 0.66 0.94 1.06 1.15 0.87

0296 SC4 05 116 0.65 1.61 0.90 1.01 0.99 0.45 2.24

0297 RES 09 73 1.03 1.48 0.65 0.94 1.06 1.01 0.99

0298 RES 01 53 2.43 0.59 0.60 0.96 1.05 1.35 0.74

0299 LP 01 674 1.25 0.82 0.97 1.07 0.93 1.83 0.55

0300 LP 9.1 27,192 1.13 0.84 1.04 1.00 1.00 1.00 1.00

0301 IS 10 4,358 1.22 0.80 1.00 1.66 0.60 1.42 0.70

0302 ES1 16.1 14,986 0.93 0.93 1.14 1.82 0.55 2.01 0.50

0303 LP 01 6,320 1.23 0.79 1.00 1.13 0.88 1.40 0.72

0304 LP 16.1 12,572 1.12 0.94 0.96 1.33 0.75 1.28 0.78

0305 LP 16.1 2,532 0.99 1.06 0.96 2.07 0.48 1.56 0.64

0306 SC 05 41 0.78 1.34 0.94 1.00 1.00 0.35 2.89

0307 RES 07 35 1.04 1.30 0.75 0.93 1.08 1.45 0.69

0308 SC4 05 261 0.69 1.42 0.96 1.00 1.00 0.63 1.60

0309 RES 7.1 0 1.04 0.69 1.36 0.96 1.04 1.54 0.65

0310 RES 04 66 1.01 1.20 0.84 1.06 0.94 1.66 0.60

0311 RES 07 87 1.13 1.12 0.81 1.05 0.95 1.27 0.79

0312 RES 15 72 0.84 1.63 0.72 0.86 1.16 1.09 0.92

0313 LP1 16.1 8,812 1.23 0.74 1.07 1.34 0.75 1.37 0.73

0314 LP1 16.1 6,663 0.78 1.09 1.13 1.69 0.59 1.34 0.75

0315 SC4 01 143 1.57 0.82 0.78 0.92 1.09 2.14 0.47

0316 RT0 07 35 1.51 0.88 0.70 0.99 1.01 1.28 0.78

0317 RES 06 22 0.52 3.22 0.45 0.90 1.11 1.05 0.95

0318 RES 01 37 2.18 0.60 0.68 1.01 0.99 1.33 0.75

0319 RES 04 55 0.90 1.43 0.76 0.88 1.14 1.58 0.63

0320 RES 01 565 1.20 0.95 0.89 0.99 1.01 1.78 0.56

0321 RES 04 66 0.78 1.65 0.73 0.99 1.01 1.19 0.84

0322 SC4 01 147 1.57 0.75 0.85 1.14 0.87 2.64 0.38

0323 RES 07 26 0.98 1.58 0.64 0.94 1.07 1.58 0.63

0324 RES 10 521 1.31 0.80 0.96 1.44 0.69 1.94 0.52

0325 RES 16 53 0.78 1.89 0.63 0.87 1.15 1.13 0.89

0326 SC 16 41 1.25 1.03 0.80 1.78 0.56 1.87 0.53

0327 RES 01 48 1.58 0.74 0.82 1.02 0.98 1.57 0.64

0328 LP 09 1,802 1.10 1.20 0.77 1.01 0.99 0.91 1.10

0329 LP5 9.1 12,354 1.05 0.93 1.01 1.06 0.95 1.02 0.98


43


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0330 LP5 03 12,888 1.15 0.94 0.93 1.03 0.97 1.04 0.96

0331 LP 10 262 1.55 0.72 0.86 2.62 0.38 2.94 0.34

0332 LP1 10 1,779 1.40 0.74 0.94 1.30 0.77 2.55 0.39

0333 RES 16.1 69 1.09 0.81 1.12 1.67 0.60 1.97 0.51

0334 LP 01 282 1.55 0.69 0.92 1.04 0.96 2.43 0.41

0335 RES 07 77 1.13 1.04 0.87 1.02 0.98 1.30 0.77

0336 SC 16.1 100 1.03 0.91 1.05 1.44 0.70 1.46 0.69

0337 SC5 08 75 1.14 1.29 0.69 0.99 1.02 0.54 1.86

0338 SC5 7.1 114 0.88 1.08 1.04 0.98 1.02 1.16 0.86

0339 LP 04 461 0.80 1.68 0.72 1.01 0.99 1.31 0.76

0340 FL1 16.1 24 0.81 0.56 1.46 2.10 0.48 1.12 0.89

0341 FL1 17.1 875 1.45 0.41 1.34 1.43 0.70 0.43 2.34

0342 LP1 13 348 0.81 1.32 0.92 3.05 0.33 4.38 0.23

0343 FL1 18.1 49 1.10 0.54 1.46 1.74 0.58 1.07 0.93

0344 LP1 16.1 7,856 0.98 1.07 0.95 1.88 0.53 1.88 0.53

0345 LP1 16.1 2,381 1.05 0.90 1.05 2.00 0.50 2.39 0.42

0346 SC5 04 309 1.01 1.13 0.88 1.00 1.00 1.28 0.78

0347 SC1 06 28 0.13 2.97 0.77 0.95 1.05 0.99 1.01

0348 RES 01 62 2.06 0.61 0.72 0.95 1.06 1.43 0.70

0349 RES 04 62 0.68 2.02 0.65 1.09 0.92 1.34 0.75

0350 RES 07 18 1.10 1.07 0.86 0.97 1.03 1.34 0.74

0351 SC4 7.1 217 0.98 1.03 1.00 0.92 1.09 1.73 0.58

0352 RES 10 18 1.94 0.66 0.75 0.78 1.28 1.29 0.78

0353 LP1 03 1,167 1.34 0.81 0.91 0.95 1.05 1.09 0.92

0354 LP 01 3,719 1.62 0.70 0.87 1.01 0.99 1.30 0.77

0355 RES 07 786 1.14 1.27 0.69 1.10 0.91 1.56 0.64

0356 SC4 12 259 1.16 1.03 0.86 1.31 0.77 1.09 0.91

0357 RES 01 346 1.48 0.81 0.85 0.98 1.02 1.54 0.65

0358 SC4 10 236 1.41 0.76 0.92 1.28 0.78 1.62 0.62

0359 RES 01 38 1.44 0.88 0.80 1.02 0.98 1.35 0.74

0360 RES 09 40 0.71 2.31 0.54 0.95 1.05 0.96 1.04

0361 RES 07 25 1.52 0.94 0.70 1.05 0.95 1.30 0.77

0362 RES 16 41 1.42 1.05 0.68 0.82 1.21 1.22 0.82

0363 RES 07 36 0.97 1.59 0.64 1.06 0.94 1.12 0.89

0364 RES 01 64 1.95 0.59 0.78 1.00 1.00 1.81 0.55

0365 LP 7.1 5,716 1.11 0.92 0.98 1.01 0.99 1.29 0.78

0366 LP 7.1 5,262 1.03 1.04 0.94 1.00 1.00 1.34 0.75

0367 LP 7.1 4,714 1.10 0.90 1.00 1.01 0.99 1.35 0.74

0368 LP 7.1 5,976 1.11 0.91 0.99 1.02 0.98 1.30 0.77

0369 LP 7.1 4,825 1.03 1.06 0.92 1.01 0.99 1.19 0.84


44


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0370 LP 01 3,322 1.30 0.84 0.91 1.01 0.99 1.17 0.86

0371 RES 01 67 1.94 0.52 0.86 0.91 1.10 1.80 0.56

0372 LP1 16.1 2,751 0.94 0.91 1.14 2.92 0.34 2.56 0.39

0373 LP 10 5,642 1.09 1.03 0.90 1.73 0.58 2.50 0.40

0374 RES 01 53 1.75 0.74 0.74 0.98 1.03 1.77 0.57

0375 SC5 04 124 0.78 1.23 1.00 0.95 1.05 1.17 0.86

0376 LP 01 151 1.49 0.71 0.91 1.06 0.95 1.31 0.77

0377 LP 15 108 0.59 2.55 0.55 1.27 0.79 0.96 1.04

0378 RES 07 50 0.84 1.80 0.62 0.99 1.01 1.29 0.78

0379 RT0 07 28 1.33 1.36 0.58 1.12 0.89 1.12 0.89

0380 LP1 16.1 4,824 0.82 1.04 1.13 1.91 0.52 2.10 0.48

0381 UNK 16.1 3,251 1.00 0.80 1.21 2.63 0.38 2.99 0.33

0382 FL1 17.1 761 0.72 1.03 1.27 1.43 0.70 0.85 1.18

0383 LP 8.1 194 1.03 0.44 1.67 1.13 0.88 0.71 1.41

0384 SC4 N/A 0 1.11 1.39 0.55 0.88 1.14 NaN NaN

0385 RES 10 22 2.49 0.60 0.57 0.86 1.17 1.20 0.83

0386 SC4 7.1 211 1.17 0.87 0.98 1.03 0.97 1.23 0.81

0387 RES 04 45 0.93 1.20 0.93 1.00 1.00 1.29 0.77

0388 LP 05 285 0.91 1.20 0.92 1.00 1.00 0.11 8.91

0389 LP 01 865 1.52 0.74 0.87 1.14 0.88 3.07 0.33

0390 LP2 9.1 6,776 1.07 0.92 1.02 1.01 0.99 0.99 1.01

0391 LP 10 4,302 1.53 0.76 0.84 1.50 0.67 1.54 0.65

0392 UNK 15 3,243 0.84 1.45 0.81 1.23 0.81 1.07 0.94

0393 RES 07 67 1.62 0.97 0.63 0.93 1.07 1.18 0.85

0394 SC4 05 87 0.43 2.42 0.70 0.96 1.04 0.36 2.76

0395 RES 16 34 1.78 0.84 0.64 0.82 1.22 1.44 0.69

0396 RES 04 70 0.81 1.41 0.85 1.02 0.98 1.41 0.71

0397 RES 07 44 0.86 1.60 0.70 0.92 1.08 1.12 0.89

0398 RES 01 30 1.88 0.62 0.79 1.11 0.90 1.40 0.71

0399 SC1 09 30 1.01 1.39 0.72 0.99 1.01 1.06 0.94

0400 RES 09 54 1.21 1.49 0.56 0.92 1.08 1.08 0.93

0401 RES 07 84 1.29 1.04 0.77 1.04 0.96 1.50 0.67

0402 RES 07 43 1.09 1.33 0.70 1.10 0.91 1.53 0.66

0403 RES 08 53 1.05 1.54 0.62 0.99 1.01 0.80 1.25

0404 RES 07 37 1.49 1.03 0.67 0.96 1.04 1.54 0.65

0405 RES 13 72 0.84 1.45 0.82 0.77 1.29 1.41 0.71

0406 RES 09 164 0.98 1.64 0.62 0.97 1.03 1.04 0.97

0407 RES 01 35 2.22 0.55 0.70 1.03 0.97 1.38 0.73

0408 LP 15 6,325 0.93 1.24 0.87 1.16 0.86 1.04 0.96

0409 RES 01 53 1.70 0.77 0.74 0.96 1.04 1.24 0.81


45


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0410 LP 10 807 1.38 0.93 0.80 1.67 0.60 1.93 0.52

0411 LP 7.1 11,121 1.12 0.92 0.97 1.01 0.99 1.21 0.82

0412 LP 03 9,683 1.18 0.87 0.98 1.02 0.98 1.10 0.91

0413 LP 9.1 6,993 1.11 0.89 1.00 1.01 0.99 1.09 0.92

0414 LP 7.1 9,466 1.12 0.88 1.01 1.01 0.99 1.21 0.83

0415 LP 01 9,756 1.19 0.86 0.98 1.01 0.99 1.11 0.90

0416 LP 01 10,257 1.22 0.84 0.97 1.01 0.99 1.18 0.85

0417 RES 10 37 2.10 0.63 0.73 0.84 1.18 1.53 0.65

0418 LP1 16.1 4,745 0.97 0.99 1.04 1.57 0.64 1.97 0.51

0419 UNK 03 10,913 1.59 0.66 0.90 1.04 0.96 1.05 0.95

0420 GMU 01 48,231 1.31 0.77 0.96 1.11 0.90 1.25 0.80

0421 LP 10 1,974 1.32 0.76 0.94 0.84 1.19 1.89 0.53

0422 RES 02 60 2.36 0.57 0.64 0.95 1.05 0.87 1.16

0423 RES 01 361 1.68 0.72 0.82 0.91 1.09 1.86 0.54

0424 SC4 10 688 1.30 0.86 0.90 1.42 0.71 1.57 0.64

0425 RES 16 85 1.00 1.48 0.67 0.86 1.16 1.29 0.78

0426 RES 16 111 0.85 1.67 0.69 0.84 1.20 1.15 0.87

0427 RES 06 87 0.90 1.46 0.75 0.92 1.09 0.94 1.06

0428 LP 01 5,796 1.28 0.82 0.95 1.15 0.87 1.56 0.64

0429 SC4 09 5 0.65 3.21 0.37 1.06 0.94 1.02 0.98

0430 SC4 16 7 1.46 1.08 0.63 1.21 0.83 1.32 0.76

0431 RES 01 42 1.71 0.84 0.71 0.98 1.02 1.52 0.66

0432 LP1 16.1 3,464 0.93 0.99 1.07 2.01 0.50 1.79 0.56

0433 LP1 16.1 2,365 1.11 0.86 1.04 3.09 0.32 2.82 0.35

0434 RES 04 90 0.61 2.25 0.62 0.90 1.12 1.18 0.85

0435 RES 01 107 1.29 0.93 0.84 1.11 0.90 1.20 0.84

0436 SC 9.1 193 1.03 0.92 1.05 1.00 1.00 1.00 1.00

0437 SC 03 101 1.25 0.85 0.96 1.02 0.98 1.07 0.93

0438 SC 9.1 605 1.18 0.82 1.02 1.02 0.98 1.07 0.94

0439 SC 03 113 1.26 0.78 1.00 1.01 0.99 1.07 0.94

0440 SC4 09 11 1.03 2.09 0.43 0.99 1.01 0.91 1.09

0441 SC 9.1 252 1.04 0.95 1.00 1.00 1.00 1.01 0.99

0442 LP 03 6,247 1.21 0.85 0.98 1.01 0.99 1.03 0.97

0443 SC 06 150 0.99 1.14 0.89 1.02 0.98 1.01 0.99

0444 SC 9.1 295 1.20 0.81 1.03 1.01 0.99 1.06 0.94

0445 SC 9.1 77 1.15 0.85 1.02 1.00 1.00 1.05 0.95

0446 RES 07 57 1.93 0.78 0.64 0.92 1.09 1.33 0.75

0447 RES 07 35 1.26 1.11 0.73 0.94 1.06 1.23 0.82

0448 RES 15 45 0.76 1.75 0.70 0.82 1.21 1.08 0.93

0449 RES 13 46 0.69 2.00 0.67 0.86 1.16 1.16 0.86


46


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0450 RES 06 76 0.65 1.86 0.74 0.98 1.03 1.07 0.93

0451 LP 01 6,845 1.20 0.87 0.97 1.01 0.99 1.27 0.79

0452 LP 01 8,728 1.21 0.86 0.96 1.01 0.99 1.26 0.79

0453 SC 09 132 1.15 1.16 0.76 1.05 0.95 1.04 0.96

0454 RES 01 1,723 1.40 0.79 0.91 0.92 1.09 1.36 0.74

0455 RES 07 76 1.20 1.16 0.73 0.98 1.02 1.21 0.82

0456 RES 05 192 0.88 1.48 0.76 0.99 1.01 0.84 1.19

0457 RES 04 40 0.66 2.27 0.58 0.89 1.13 1.16 0.86

0458 RES 01 64 1.87 0.67 0.78 1.03 0.97 1.52 0.66

0459 RES 05 485 0.82 1.47 0.82 0.97 1.03 0.72 1.39

0460 RES 07 52 1.44 1.21 0.58 0.93 1.08 1.44 0.69

0461 LP 03 5,632 1.11 1.03 0.89 0.99 1.01 0.97 1.03

0462 SC4 01 271 1.31 0.83 0.94 1.11 0.90 1.26 0.79

0463 LP 01 5,065 1.36 0.84 0.87 1.13 0.88 3.45 0.29

0464 RES 01 25 2.23 0.68 0.63 0.92 1.08 1.41 0.71

0465 RES 01 39 1.83 0.48 0.96 0.99 1.01 1.56 0.64

0466 RES 07 49 1.37 1.19 0.63 0.92 1.09 1.13 0.88

0467 RES 04 58 0.81 1.47 0.79 0.98 1.02 1.24 0.81

0468 RES 07 47 2.15 0.74 0.62 0.95 1.05 1.54 0.65

0469 FL1 14 625 0.53 1.59 1.05 1.22 0.82 0.85 1.18

0470 RES 01 1,167 1.24 0.79 1.03 1.06 0.95 1.73 0.58

0471 SC4 03 175 2.27 0.66 0.62 0.98 1.02 1.07 0.93

0472 LP1 16.1 3,832 1.28 0.66 1.11 2.50 0.40 2.56 0.39

0473 LP1 16.1 4,953 0.81 1.04 1.15 1.90 0.53 1.97 0.51

0474 RES 07 47 1.17 1.22 0.72 0.93 1.07 1.70 0.59

0475 RES 04 41 0.94 1.18 0.89 1.12 0.89 1.40 0.72

0476 RES 09 48 0.84 1.85 0.60 1.01 0.99 0.97 1.03

0477 RES 09 101 0.92 1.58 0.67 1.02 0.98 1.08 0.93

0478 UNK 08 178 1.37 1.00 0.75 0.98 1.03 0.82 1.22

0479 LP 09 2,990 1.33 0.97 0.80 1.00 1.00 1.07 0.93

0480 LP 09 2,682 1.24 1.05 0.79 1.04 0.96 1.01 0.99

0481 LP 09 2,680 1.17 1.12 0.78 1.03 0.97 0.99 1.01

0482 RES 01 36 1.36 0.91 0.83 1.06 0.94 1.62 0.62

0483 LP 10 6,410 1.23 0.80 1.00 1.18 0.84 1.86 0.54

0484 LP 01 7,004 1.26 0.78 0.99 1.13 0.89 1.82 0.55

0485 RES 01 62 2.26 0.52 0.72 0.95 1.05 1.68 0.60

0486 RES 01 42 1.23 0.85 0.97 0.98 1.02 1.34 0.75

0487 SC 16 28 1.37 1.00 0.75 1.45 0.69 1.51 0.66

0488 RES 01 20 1.61 0.86 0.75 0.94 1.06 1.43 0.70

0489 RES 15 70 0.58 2.32 0.61 0.86 1.16 1.10 0.91


47


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0490 LP 03 5,323 1.41 0.78 0.89 0.89 1.12 1.08 0.92

0491 RES 10 49 1.72 0.60 0.92 1.15 0.87 1.30 0.77

0492 RES 7.1 35 0.92 1.14 0.95 0.93 1.07 1.39 0.72

0493 RES 03 43 1.53 0.73 0.88 1.06 0.94 1.08 0.92

0494 RES 06 66 0.72 1.89 0.67 0.95 1.05 1.05 0.96

0495 RES 01 51 1.64 0.82 0.77 0.91 1.09 1.21 0.83

0496 LP1 16.1 15,760 1.14 0.79 1.08 1.57 0.64 1.57 0.64

0497 RES 04 1,967 0.69 1.85 0.70 0.91 1.09 1.38 0.72

0498 SC4 13 186 0.67 1.72 0.82 1.57 0.64 1.49 0.67

0499 RES 07 197 1.13 1.31 0.69 0.93 1.07 1.18 0.85

0500 LP 01 6,866 2.19 0.40 0.89 1.00 1.00 2.14 0.47

0501 LP 7.1 11,787 0.96 1.05 0.99 0.99 1.01 1.45 0.69

0502 LP 9.1 8,694 1.02 0.93 1.04 1.13 0.88 1.00 1.00

0503 UNK 13 27,529 0.87 1.35 0.85 1.25 0.80 1.21 0.83

0504 RES 13 42 0.86 1.49 0.77 0.85 1.17 1.46 0.68

0505 SC4 16.1 38 1.22 0.74 1.09 1.22 0.82 1.21 0.83

0506 LP 01 2,174 1.29 0.83 0.93 1.02 0.98 1.21 0.82

0507 LP 01 26,877 1.29 0.84 0.92 1.03 0.97 1.29 0.78

0508 RES 07 56 1.03 1.33 0.74 0.96 1.04 1.26 0.79

0509 SC4 05 298 0.84 1.22 0.97 1.05 0.95 0.59 1.70

0510 SC 16.1 56 1.04 1.08 0.90 2.35 0.43 2.67 0.37

0511 RES 07 40 0.87 1.60 0.69 0.99 1.01 1.27 0.79

0512 RES 09 27 1.15 1.09 0.82 1.05 0.96 1.01 0.99

0513 RES 06 66 0.61 2.26 0.61 0.93 1.07 1.05 0.95

0514 LP 01 4,759 1.48 0.71 0.92 1.10 0.91 2.12 0.47

0515 LP 16.1 703 1.08 0.93 1.00 2.06 0.48 3.76 0.27

0516 LP 13 224 0.90 1.31 0.85 3.10 0.32 2.41 0.41

0517 LP 13 537 0.92 1.36 0.80 1.65 0.61 1.56 0.64

0518 RES 16 25 1.11 1.09 0.84 0.83 1.20 1.71 0.59

0519 SC4 01 292 1.18 0.87 0.98 1.05 0.95 1.23 0.81

0520 LP 01 250 1.50 0.71 0.93 1.01 0.99 1.66 0.60

0521 LP 01 831 1.41 0.69 0.97 1.12 0.90 2.31 0.43

0522 SC 03 97 1.71 0.64 0.89 1.02 0.98 1.06 0.95

0523 LP 16.1 1,434 0.93 1.04 1.02 6.41 0.16 11.28 0.09

0524 LP 16.1 1,578 0.95 1.06 0.99 4.60 0.22 6.26 0.16

0525 LP 16.1 544 1.02 0.88 1.12 5.23 0.19 3.64 0.27

0526 RES 03 21 1.91 0.77 0.67 0.91 1.10 1.08 0.92

0527 RES 04 83 0.62 1.99 0.70 1.06 0.94 1.20 0.83

0528 LP 01 1,360 1.23 0.90 0.90 0.95 1.05 1.57 0.64

0529 LP 13 3,949 0.95 1.18 0.90 1.67 0.60 1.83 0.55


48


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0530 SC 16 122 1.02 1.48 0.66 1.30 0.77 1.48 0.68

0531 LP 7.1 3,572 1.13 0.94 0.95 1.07 0.93 1.34 0.74

0532 RES 10 27 1.63 0.75 0.79 0.86 1.16 1.53 0.65

0533 RES 01 30 1.67 0.81 0.76 0.92 1.09 1.41 0.71

0534 RT0 07 18 1.48 0.93 0.71 1.01 0.99 1.50 0.67

0535 RES 02 32 1.37 0.89 0.83 0.93 1.08 0.86 1.16

0536 RES N/A 3 2.02 NaN 0.50 1.05 0.95 1.31 0.77

0537 RES 01 42 1.97 0.65 0.72 0.97 1.03 1.81 0.55

0538 SC4 10 44 1.30 0.86 0.89 1.64 0.61 2.28 0.44

0539 RES 09 94 0.99 1.33 0.76 0.97 1.03 1.03 0.97

0540 RES 10 54 2.12 0.66 0.70 0.85 1.18 1.19 0.84

0541 RES 01 49 1.61 0.78 0.77 0.90 1.11 1.23 0.81

0542 FL1 17.1 319 0.50 1.05 1.58 1.39 0.72 0.90 1.11

0543 LP1 16.1 4,950 0.81 1.07 1.12 1.85 0.54 1.75 0.57

0544 LP1 10 3,329 1.63 0.45 1.13 4.85 0.21 5.31 0.19

0545 SC4 13 64 1.01 1.11 0.90 1.30 0.77 1.38 0.72

0546 RT0 09 9 2.17 0.74 0.56 0.93 1.08 0.96 1.04

0547 LP 01 3,390 1.29 0.80 0.96 1.10 0.91 2.07 0.48

0548 RES 01 112 1.29 0.93 0.85 0.99 1.01 1.20 0.83

0549 RT0 09 27 1.14 1.38 0.66 0.96 1.04 0.98 1.02

0550 LP 01 384 1.52 0.73 0.90 1.15 0.87 1.55 0.65

0551 RES 07 120 1.09 1.13 0.83 0.96 1.05 1.33 0.75

0552 LP 01 2,784 1.38 0.77 0.94 1.01 0.99 1.58 0.63

0553 LP 01 4,076 1.22 0.87 0.95 1.02 0.98 1.46 0.68

0554 RES 05 127 0.72 1.91 0.65 1.02 0.98 0.79 1.27

0555 LP 10 2,875 1.26 0.89 0.91 1.24 0.81 1.28 0.78

0556 RES 08 53 1.97 0.89 0.54 0.91 1.09 0.89 1.13

0557 RES 10 33 1.71 0.81 0.74 0.86 1.17 1.62 0.62

0558 LP 10 3,031 1.28 0.82 0.95 1.27 0.79 1.68 0.60

0559 RES 07 86 1.07 1.58 0.58 0.95 1.06 1.11 0.90

0560 RES 07 144 0.94 1.55 0.68 0.99 1.01 1.17 0.85

0561 IS 10 10,262 1.17 0.88 0.96 1.27 0.79 2.62 0.38

0562 SC4 10 65 2.08 0.52 0.79 1.17 0.85 2.28 0.44

0563 RES 7.1 46 0.97 1.10 0.94 1.01 0.99 1.57 0.64

0564 LP 01 4,310 1.38 0.82 0.89 0.94 1.06 1.25 0.80

0565 LP1 16.1 2,754 1.08 0.90 1.01 1.56 0.64 1.74 0.58

0566 LP1 10 2,640 1.70 0.50 0.90 1.57 0.64 2.32 0.43

0567 LP1 08 265 0.93 1.42 0.74 0.98 1.02 0.74 1.35

0568 LP1 05 201 0.77 1.41 0.90 0.97 1.03 0.70 1.42

0569 FL1 05 675 0.41 2.99 0.73 1.04 0.96 0.87 1.15


49


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0570 LP1 16.1 5,497 0.99 0.83 1.19 2.07 0.48 2.53 0.39

0571 ES1 16.1 2,796 0.95 0.95 1.10 2.51 0.40 2.49 0.40

0572 LP1 16.1 2,542 0.98 0.86 1.16 2.85 0.35 2.48 0.40

0573 LP1 16.1 2,781 0.82 1.08 1.10 2.43 0.41 2.05 0.49

0574 LP1 16.1 2,500 0.98 0.89 1.12 2.53 0.40 2.18 0.46

0575 LP1 16.1 2,526 0.99 0.96 1.04 2.55 0.39 2.35 0.43

0576 LP1 10 2,949 1.51 0.58 0.98 1.67 0.60 1.75 0.57

0577 LP1 16.1 3,070 1.06 0.84 1.09 1.87 0.53 2.10 0.48

0578 SC4 01 202 1.20 0.99 0.86 1.05 0.95 1.53 0.66

0579 RES 03 13 1.59 0.89 0.77 0.94 1.06 1.08 0.92

0580 RES 15 30 0.56 2.68 0.58 0.82 1.22 1.04 0.96

0581 RES 06 58 0.70 2.09 0.61 0.95 1.06 1.10 0.91

0582 RES 16 54 0.97 1.61 0.63 0.78 1.28 1.20 0.83

0583 RES 07 69 1.45 0.92 0.75 0.95 1.05 1.14 0.88

0584 RES 09 89 0.89 1.61 0.68 0.95 1.05 1.01 0.99

0585 LP1 16.1 3,158 0.87 0.93 1.20 2.22 0.45 2.48 0.40

0586 RES 09 96 0.91 1.59 0.68 1.04 0.97 1.07 0.93

0587 SC 04 156 0.57 2.06 0.71 1.10 0.91 1.21 0.82

0588 ES1 16.1 2,922 0.97 0.92 1.10 2.76 0.36 2.43 0.41

0589 LP1 13 1,828 1.02 1.11 0.89 1.26 0.80 2.25 0.44

0590 LP 01 8,446 1.24 0.82 0.98 1.04 0.97 1.21 0.83

0591 RES 16 36 1.15 1.13 0.79 0.38 2.65 1.68 0.60

0592 LP1 16.1 3,554 0.78 1.01 1.21 1.54 0.65 1.38 0.73

0593 SC 05 27 0.89 1.11 0.99 1.01 0.99 0.32 3.12

0594 RES 04 207 0.90 1.29 0.86 0.98 1.02 1.25 0.80

0595 SC4 10 60 1.56 0.84 0.76 1.70 0.59 2.03 0.49

0596 RES 06 55 0.52 2.36 0.65 0.94 1.06 0.93 1.08

0597 RES 01 29 1.28 0.95 0.85 0.99 1.01 1.36 0.73

0598 RES 10 119 1.40 0.94 0.78 1.25 0.80 1.46 0.69

0599 SC4 7.1 69 1.17 0.86 1.00 0.94 1.07 1.60 0.63

0600 SC4 10 56 1.30 0.78 0.98 1.23 0.81 2.09 0.48

0601 RES 03 19 2.83 0.41 0.66 1.02 0.98 0.97 1.04

0602 RES 07 16 1.75 0.84 0.69 1.06 0.94 1.17 0.86

0603 ES1 13 5,560 0.74 1.34 0.94 2.37 0.42 2.23 0.45

0604 ES1 13 310 0.59 1.37 1.05 2.52 0.40 2.58 0.39

0605 RES 01 57 1.86 0.70 0.77 0.94 1.06 1.11 0.90

0606 RES 07 8 1.27 1.01 0.81 1.01 0.99 1.30 0.77

0607 RES 01 54 1.98 0.66 0.75 0.93 1.08 1.60 0.63

0608 RT0 07 11 1.61 1.04 0.59 0.93 1.08 1.59 0.63

0609 UNK 16.1 1,734 0.93 1.04 1.02 1.31 0.76 1.64 0.61


50


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0610 UNK 10 3,168 1.13 0.99 0.91 0.85 1.18 3.99 0.25

0611 IS 16.1 3,578 1.08 0.98 0.96 6.44 0.16 8.51 0.12

0612 RES 01 16 2.29 0.66 0.66 0.96 1.04 1.45 0.69

0613 RES 10 3,226 1.43 0.84 0.85 0.64 1.55 1.86 0.54

0614 LP 10 6,850 1.34 0.76 0.99 1.16 0.86 1.85 0.54

0615 LP 01 8,158 1.27 0.82 0.98 1.07 0.93 1.52 0.66

0616 LP 03 600 1.38 0.90 0.84 0.89 1.12 1.01 0.99

0617 IS 16.1 13,610 1.15 0.88 1.00 3.88 0.26 5.07 0.20

0618 LP 02 422 1.22 0.95 0.91 0.98 1.02 0.84 1.18

0619 LP 06 1,023 0.98 1.27 0.82 0.97 1.03 1.00 1.00

0620 RES 18 62 1.22 1.49 0.55 0.82 1.21 1.08 0.93

0621 RES 07 50 0.96 1.41 0.74 0.89 1.13 1.17 0.86

0622 RES 01 35 2.17 0.70 0.66 1.00 1.00 1.77 0.57

0623 LP 16 60 1.11 1.34 0.69 1.79 0.56 2.43 0.41

0624 SC4 10 69 1.63 0.80 0.80 2.09 0.48 2.46 0.41

0625 RES 07 32 1.47 0.99 0.72 0.97 1.04 1.27 0.79

0626 UNK 10 1,063 1.24 0.92 0.90 1.21 0.82 1.25 0.80

0627 RES 10 43 2.12 0.60 0.77 0.80 1.25 1.41 0.71

0628 SC4 10 151 1.77 0.65 0.87 1.41 0.71 1.84 0.54

0629 LP 07 2,881 0.98 1.35 0.76 0.99 1.01 1.19 0.84

0630 SC2 13 137 0.89 1.23 0.89 1.33 0.75 1.57 0.64

0631 LP5 16.1 3,365 0.90 1.10 1.00 2.07 0.48 1.62 0.62

0632 UNK 10 529 1.49 0.79 0.88 1.16 0.86 1.97 0.51

0633 LP 07 1,588 1.17 1.14 0.77 0.99 1.01 1.40 0.71

0634 RES 07 36 1.76 1.01 0.58 0.95 1.05 1.32 0.76

0635 LP5 06 9,130 0.91 1.37 0.79 1.04 0.96 0.96 1.04

0636 RES 01 21 1.51 0.90 0.77 0.98 1.02 1.50 0.66

0637 RES 01 40 1.64 0.86 0.72 1.04 0.96 1.67 0.60

0638 RES 03 31 1.93 0.73 0.72 0.96 1.04 1.00 1.00

0639 ES1 13 3,711 0.66 1.46 0.93 3.09 0.32 2.25 0.44

0640 LP 01 2,699 1.27 0.83 0.97 1.13 0.89 2.69 0.37

0641 LP 03 5,230 1.36 0.68 1.13 1.05 0.96 1.05 0.96

0642 RES 03 39 1.90 0.67 0.77 0.94 1.06 0.91 1.10

0643 RES 06 124 0.66 2.10 0.66 1.00 1.00 0.91 1.10

0644 RT0 07 28 1.09 1.82 0.50 1.12 0.89 1.17 0.85

0645 RES 04 86 0.69 1.93 0.68 1.01 0.99 1.34 0.75

0646 SC4 7.1 76 1.03 1.05 0.94 0.89 1.13 3.04 0.33

0647 UNK 09 14 1.01 1.16 0.84 1.02 0.98 1.05 0.95

0648 RES 18 26 1.41 0.96 0.78 0.79 1.26 1.06 0.94

0649 RES 07 24 1.85 0.84 0.67 0.99 1.01 1.33 0.75


51


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0650 SC 13 4 0.28 1.91 1.10 1.44 0.69 5.16 0.19

0651 RES 07 19 1.47 1.21 0.58 0.96 1.04 1.22 0.82

0652 RES 07 90 1.48 1.14 0.62 0.98 1.02 1.21 0.83

0653 RES 04 71 0.72 1.73 0.73 1.02 0.98 1.26 0.80

0654 RES 10 17 1.82 0.69 0.80 0.86 1.16 1.12 0.89

0655 RES 07 55 1.45 0.94 0.77 0.95 1.05 1.31 0.77

0656 RES 09 102 0.90 1.50 0.73 0.96 1.04 1.05 0.95

0657 RES 16 36 1.26 1.10 0.75 0.84 1.19 1.32 0.76

0658 RES 01 25 1.65 0.78 0.81 1.05 0.95 1.55 0.64

0659 RES 08 54 0.85 2.27 0.44 0.95 1.06 0.80 1.25

0660 RES 04 50 0.79 1.50 0.81 0.98 1.02 1.12 0.90

0661 LP 01 693 1.20 0.88 0.96 0.93 1.07 1.96 0.51

0662 RES 03 6 1.48 0.64 0.89 0.98 1.02 1.04 0.97

0663 LP 18.1 5,619 1.05 1.01 0.96 1.17 0.86 1.02 0.98

0664 LP 03 4,952 1.30 0.83 0.95 1.05 0.96 1.06 0.95

0665 LP 10 334 1.10 1.04 0.90 1.71 0.58 2.03 0.49

0666 RES 07 42 0.81 1.79 0.64 0.88 1.13 1.36 0.73

0667 RES 01 85 1.48 0.83 0.83 0.98 1.02 1.60 0.63

0668 SC 9.1 13 1.01 1.00 1.00 1.00 1.00 1.00 1.00

0669 SC 9.1 20 1.01 0.99 1.00 1.00 1.00 1.00 1.00

0670 LP 10 7,101 1.26 0.79 0.97 1.34 0.74 1.16 0.86

0671 RES 02 114 1.10 1.02 0.90 1.01 0.99 0.82 1.22

0672 UNK 07 190 1.10 1.13 0.82 1.08 0.93 1.78 0.56

0673 RES 07 65 0.87 1.77 0.61 0.95 1.06 1.20 0.83

0674 RES 01 35 1.38 0.91 0.83 0.94 1.07 1.30 0.77

0675 RES 01 63 1.75 0.65 0.89 0.96 1.04 1.28 0.78

0676 RES 13 15 0.89 1.31 0.85 0.85 1.17 1.16 0.86

0677 RES 07 22 1.81 1.03 0.55 1.02 0.98 1.16 0.86

0678 ES1 16 4,838 1.05 1.14 0.85 1.80 0.56 2.42 0.41

0679 LP 16 4,672 1.04 1.28 0.76 1.58 0.63 1.85 0.54

0680 LP 16 2,020 0.71 2.16 0.57 1.50 0.66 1.68 0.60

0681 RES 07 29 1.94 0.81 0.64 0.97 1.03 1.52 0.66

0682 RES 03 29 2.33 0.61 0.69 0.88 1.13 1.03 0.97

0683 RES 07 25 1.49 0.95 0.74 0.92 1.08 1.32 0.76

0684 LP 16.1 346 0.93 1.12 0.96 1.83 0.55 1.78 0.56

0685 RES 01 56 1.93 0.75 0.71 0.87 1.15 1.23 0.81

0686 LP 16.1 1,685 1.05 1.07 0.91 1.31 0.77 1.50 0.67

0687 LP 7.1 241 0.84 1.08 1.06 1.02 0.98 2.17 0.46

0688 RES 10 200 1.73 0.83 0.73 1.47 0.68 2.10 0.48

0689 RES 07 1,615 0.97 1.63 0.62 0.92 1.09 1.11 0.90


52


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0690 SC6 06 19 0.30 3.42 0.53 1.00 1.00 1.03 0.97

0691 SC6 13 79 0.41 2.22 0.76 1.41 0.71 1.39 0.72

0692 LP5 13 3,089 0.90 1.24 0.88 1.97 0.51 1.95 0.51

0693 LP5 16.1 52 0.51 0.63 2.28 1.66 0.60 1.29 0.78

0694 LP5 13 349 0.49 1.52 1.08 1.82 0.55 1.71 0.59

0695 LP5 16.1 4,167 0.75 1.09 1.15 1.70 0.59 1.71 0.58

0696 LP5 10 3,279 2.00 0.42 1.01 3.32 0.30 2.63 0.38

0697 RES 01 31 2.29 0.58 0.72 0.92 1.08 1.49 0.67

0698 RES 07 168 0.92 1.54 0.69 0.90 1.11 1.13 0.88

0699 RES 06 71 0.52 2.52 0.60 1.00 1.00 0.98 1.02

0700 UNK 06 108 0.64 1.77 0.77 0.96 1.05 1.08 0.93

0701 SC4 07 26 0.98 1.89 0.51 1.14 0.88 1.45 0.69

0702 SC 05 10 0.62 1.32 1.06 0.99 1.01 0.73 1.36

0703 LP 7.1 457 1.20 0.83 1.02 1.07 0.93 1.65 0.61

0704 RES 07 39 1.92 0.87 0.62 0.90 1.11 1.43 0.70

0705 RES 01 34 2.30 0.43 0.89 0.99 1.01 1.50 0.67

0706 RES 01 26 4.18 0.36 0.49 1.10 0.91 1.56 0.64

0707 LP 01 207 1.26 0.99 0.84 1.12 0.89 1.33 0.75

0708 RES 01 30 2.13 0.67 0.70 0.96 1.04 1.87 0.54

0709 RES 01 35 1.76 0.81 0.72 0.93 1.07 1.76 0.57

0710 RES 01 68 1.34 0.88 0.87 0.91 1.10 1.22 0.82

0711 RES 10 22 2.02 0.71 0.69 0.84 1.18 1.48 0.68

0712 SC 09 95 0.88 1.63 0.67 1.14 0.88 0.93 1.08

0713 LP 13 748 0.73 1.78 0.71 1.39 0.72 1.32 0.76

0714 LP 9.1 8,967 1.04 0.98 0.98 1.03 0.97 1.04 0.96

0715 SC4 N/A - NaN NaN NaN NaN NaN NaN NaN

0716 UNK 16.1 14,249 1.07 1.04 0.91 1.50 0.67 1.55 0.65

0717 UNK 16.1 9,655 1.10 0.97 0.95 1.44 0.70 1.45 0.69

0718 RES 04 68 0.95 1.21 0.87 0.98 1.02 1.50 0.66

0719 UNK 7.1 318 1.15 0.83 1.04 1.02 0.98 1.14 0.88

0720 SC 7.1 249 1.16 0.82 1.05 1.04 0.96 1.12 0.90

0721 UNK 01 151 1.36 0.72 1.04 1.05 0.96 1.14 0.88

0722 UNK 03 219 1.22 0.84 1.00 1.02 0.98 1.07 0.94

0723 RES 01 35 1.75 0.79 0.75 0.98 1.02 1.22 0.82

0724 RES 16 49 1.30 1.51 0.51 0.85 1.17 1.53 0.66

0725 UNK 10 6,640 1.49 0.61 1.07 1.68 0.59 2.08 0.48

0726 UNK 16.1 5,441 0.71 1.08 1.19 1.64 0.61 1.44 0.69

0727 LP5 01 15,712 1.43 0.73 0.97 1.10 0.91 1.14 0.88

0728 RES 01 82 2.79 0.53 0.62 0.94 1.07 1.31 0.77

0729 RES 01 52 1.55 0.71 0.92 0.98 1.02 1.25 0.80


53


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0730 RES 01 57 1.71 0.59 0.95 0.98 1.02 1.13 0.88

0731 RES 10 23 1.84 0.68 0.79 0.86 1.16 1.35 0.74

0732 LP 04 100 0.79 1.40 0.90 0.96 1.04 1.71 0.58

0733 RES 01 33 2.35 0.53 0.75 0.95 1.06 1.38 0.72

0734 UNK 01 421 1.90 0.49 0.97 1.08 0.92 2.32 0.43

0735 UNK 04 429 0.60 1.60 0.91 1.08 0.92 1.23 0.81

0736 LP 01 9,301 1.23 0.86 0.97 1.03 0.97 1.16 0.86

0737 LP 9.1 8,060 1.10 0.95 0.97 1.02 0.98 1.04 0.96

0738 RES 01 21 1.43 0.92 0.78 1.06 0.94 1.77 0.56

0739 RES 16 53 2.29 0.78 0.56 0.68 1.46 1.56 0.64

0740 RES 04 49 0.71 1.82 0.71 0.96 1.04 1.33 0.75

0741 SC4 8.1 7 0.26 1.37 1.48 0.98 1.02 0.90 1.11

0742 UNK 13 55 0.48 2.13 0.67 1.49 0.67 1.40 0.72

0743 RES 05 108 0.39 2.73 0.62 0.94 1.07 0.79 1.27

0744 FL1 16.1 10 0.57 0.18 4.43 8.59 0.12 1.65 0.60

0745 LP1 16.1 10,144 1.09 0.80 1.15 1.59 0.63 1.69 0.59

0746 RES 07 40 1.28 1.07 0.76 0.90 1.11 1.35 0.74

0747 RT0 05 17 0.80 1.64 0.80 0.95 1.06 0.75 1.32

0748 RES 01 37 2.41 0.69 0.59 0.98 1.02 1.67 0.60

0749 RES 13 35 0.84 1.59 0.72 0.84 1.19 1.38 0.73

0750 LP1 9.1 5,489 1.08 0.95 0.99 1.14 0.88 1.07 0.93

0751 RES 9.1 14 1.03 1.08 0.90 0.90 1.11 1.00 1.00

0752 RES 01 29 1.93 0.68 0.76 0.89 1.12 1.53 0.65

0753 RES 01 20 2.15 0.60 0.71 1.00 1.00 1.56 0.64

0754 RES 01 48 2.34 0.60 0.63 0.96 1.04 1.52 0.66

0755 RES 07 25 0.91 1.74 0.61 0.91 1.10 1.16 0.86

0756 RES 07 62 1.07 1.25 0.76 0.94 1.07 1.18 0.85

0757 RES 01 59 1.27 0.84 0.93 0.89 1.13 1.27 0.79

0758 RES 09 67 1.06 1.22 0.79 0.96 1.04 1.01 0.99

0759 RES 03 107 1.48 0.81 0.82 1.03 0.97 0.94 1.07

0760 SC4 8.1 51 1.03 1.02 0.96 1.00 1.00 0.28 3.55

0761 LP 13 2,456 0.65 2.00 0.70 1.22 0.82 1.27 0.79

0762 LP 16.1 9,650 1.00 1.04 0.96 1.55 0.64 1.75 0.57

0763 LP 13 3,840 0.87 1.44 0.79 1.49 0.67 1.90 0.53

0764 LP 01 205 1.95 0.55 0.87 1.08 0.93 3.14 0.32

0765 RES 01 53 1.55 0.77 0.84 0.95 1.05 1.22 0.82

0766 SC 9.1 312 1.11 0.95 0.96 1.00 1.00 1.03 0.97

0767 RES 07 68 1.20 1.17 0.73 1.01 0.99 1.31 0.76

0768 RES 06 38 0.79 1.66 0.72 1.14 0.88 1.07 0.94

0769 RES 07 54 0.90 1.47 0.74 0.98 1.02 1.41 0.71


54


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0770 SC4 16 38 1.05 1.65 0.57 1.39 0.72 1.29 0.77

0771 RES 09 34 0.70 2.18 0.58 0.89 1.12 0.93 1.07

0772 LP 04 301 0.95 1.21 0.87 1.07 0.93 1.65 0.61

0773 SC 12 57 1.52 0.83 0.80 1.31 0.76 1.08 0.93

0774 SC4 05 151 0.92 1.05 1.02 0.99 1.01 0.73 1.38

0775 LP 10 3,591 1.20 0.87 0.95 1.38 0.72 1.55 0.65

0776 LP 08 252 1.08 1.16 0.81 1.06 0.94 0.50 1.99

0777 LP 16.1 583 1.11 0.95 0.96 1.37 0.73 1.63 0.61

0778 SC4 7.1 208 0.99 1.02 0.99 0.93 1.08 1.67 0.60

0779 ES1 06 218 0.66 2.04 0.66 1.10 0.91 1.01 0.99

0780 SC 16 82 1.13 1.04 0.85 1.65 0.61 1.84 0.54

0781 SC4 03 2 1.61 0.40 1.25 1.13 0.89 1.05 0.95

0782 LP 12 2,498 1.31 0.81 0.89 1.46 0.69 1.03 0.97

0783 LP 14 267 1.00 1.19 0.86 1.37 0.73 0.63 1.58

0784 LP 16 4,205 1.09 1.11 0.84 1.26 0.79 1.42 0.70

0785 LP 01 14,495 1.25 0.84 0.95 1.07 0.93 1.37 0.73

0786 RES 09 63 0.95 1.49 0.69 0.94 1.06 1.04 0.96

0787 SC 02 134 1.21 0.93 0.91 1.03 0.97 0.86 1.16

0788 SC4 05 265 0.76 1.33 0.95 1.01 0.99 0.25 4.05

0789 LP 10 372 1.60 0.61 0.93 1.24 0.81 1.41 0.71

0790 LP 03 4,268 1.18 0.99 0.87 0.99 1.01 0.97 1.03

0791 SC4 05 51 0.76 1.32 0.95 1.00 1.00 0.31 3.23

0792 SC4 04 164 0.66 2.04 0.65 1.13 0.89 1.21 0.82

0793 SC5 05 43 0.44 2.85 0.55 1.03 0.97 0.83 1.20

0794 ES1 07 3,301 1.12 1.10 0.83 1.01 0.99 1.11 0.90

0795 LP1 10 1,087 1.19 0.95 0.89 1.79 0.56 1.42 0.70

0796 ES1 09 234 0.82 1.76 0.67 1.06 0.94 1.04 0.96

0797 SC1 06 10 0.11 3.18 0.82 0.98 1.02 1.01 0.99

0798 SC4 07 36 0.37 5.13 0.29 1.00 1.00 1.27 0.79

0799 SC5 07 43 1.70 1.02 0.59 1.11 0.90 1.13 0.88

0800 SC5 07 147 0.97 1.61 0.63 1.09 0.92 1.13 0.89

0801 FL1 17 12 1.08 1.39 0.81 1.84 0.54 0.30 3.34

0802 FL1 17.1 85 0.90 0.47 1.90 1.64 0.61 0.41 2.47

0803 SC1 06 88 0.38 2.70 0.66 1.14 0.88 0.95 1.05

0804 LP1 03 217 4.57 0.07 0.69 1.04 0.97 1.02 0.98

0805 SC1 04 15 0.01 2.93 0.57 1.01 0.99 1.14 0.88

0806 FL1 17.1 52 0.73 1.04 1.22 1.18 0.84 0.20 4.96

0807 LP1 07 3,500 1.14 1.09 0.83 1.08 0.92 1.28 0.78

0808 SC1 15 37 0.12 3.74 0.74 0.87 1.15 1.10 0.91

0809 FL1 16.1 21 0.05 0.55 4.95 2.89 0.35 1.34 0.74


55


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0810 FL1 17.1 320 0.70 0.83 1.53 1.55 0.64 0.42 2.41

0811 FL1 17.1 26 0.69 0.58 2.00 2.77 0.36 0.50 1.99

0812 FL1 17.1 451 0.84 0.76 1.45 1.35 0.74 0.83 1.21

0813 LP1 16.1 4,392 0.81 1.02 1.15 2.22 0.45 2.11 0.47

0814 LP1 16.1 3,734 1.07 0.83 1.11 2.20 0.45 2.22 0.45

0815 UNK 17.1 7 0.88 0.25 2.59 2.84 0.35 0.40 2.48

0816 LP1 16.1 6,863 1.00 0.93 1.07 1.37 0.73 1.32 0.76

0817 LP1 13 2,275 0.77 1.41 0.88 1.47 0.68 1.50 0.67

0818 ES1 13 563 0.59 1.80 0.81 1.40 0.71 1.37 0.73

0819 LP1 16.1 7,451 0.93 0.95 1.10 1.50 0.67 1.57 0.64

0820 SC1 14 44 0.56 2.38 0.61 1.17 0.85 0.90 1.11

0821 FL1 16.1 219 0.67 0.66 1.88 1.69 0.59 1.11 0.90

0822 FL1 05 293 0.28 1.89 1.19 0.99 1.01 0.77 1.29

0823 FL1 16.1 32 0.43 0.50 2.97 3.55 0.28 1.14 0.88

0824 FL1 15 560 0.28 2.02 1.11 1.30 0.77 1.06 0.94

0825 ES1 13 1,546 0.66 1.88 0.72 1.28 0.78 1.33 0.75

0826 LP1 16.1 3,440 0.99 1.06 0.95 1.71 0.59 1.48 0.67

0827 LP1 10 452 1.19 0.97 0.89 1.31 0.76 1.26 0.79

0828 LP1 13 530 0.65 2.14 0.63 1.30 0.77 1.11 0.90

0829 LP1 13 224 0.66 2.12 0.63 1.18 0.85 1.20 0.83

0830 SC5 05 130 0.38 2.21 0.83 1.06 0.94 0.71 1.41

0831 SC5 14 167 0.59 2.15 0.67 1.16 0.86 0.81 1.23

0832 LP1 04 405 0.75 1.89 0.65 1.12 0.89 1.13 0.88

0833 LP1 16.1 15,592 1.04 1.05 0.93 1.39 0.72 1.64 0.61

0834 SC1 06 69 0.73 2.01 0.62 1.11 0.90 1.08 0.92

0835 LP1 13 4,873 0.83 1.35 0.91 1.76 0.57 1.78 0.56

0836 FL1 17.1 61 0.82 0.60 1.73 3.06 0.33 0.48 2.06

0837 LP1 16.1 5,082 1.19 0.77 1.09 1.54 0.65 1.55 0.65

0838 LP1 10 648 2.27 0.22 1.19 3.70 0.27 2.98 0.34

0839 LP1 16.1 1,712 0.85 1.06 1.08 2.05 0.49 1.87 0.53

0840 LP1 16.1 2,713 1.09 0.87 1.06 1.78 0.56 1.60 0.63

0841 ES1 13 3,333 0.78 1.46 0.84 1.51 0.66 1.95 0.51

0842 ES1 13 1,066 0.56 1.67 0.91 1.46 0.69 1.54 0.65

0843 FL1 13 220 0.45 1.82 1.01 1.28 0.78 1.13 0.88

0844 LP1 16.1 5,643 1.16 0.84 1.02 1.21 0.83 1.23 0.81

0845 LP1 16.1 2,830 1.02 0.91 1.07 2.30 0.43 2.19 0.46

0846 ES1 13 639 0.47 2.46 0.66 1.17 0.86 1.10 0.91

0847 LP1 16.1 3,367 1.00 0.91 1.08 1.38 0.72 1.40 0.72

0848 ES1 10 3,917 1.19 0.84 0.98 1.25 0.80 1.23 0.82

0849 LP1 16.1 4,025 1.13 0.81 1.09 1.86 0.54 1.85 0.54


56


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0850 FL1 18.1 305 0.70 0.92 1.42 1.26 0.79 0.95 1.05

0851 UNK 16.1 2,710 1.12 0.76 1.15 1.83 0.54 1.68 0.59

0852 LP1 04 1,208 0.48 2.32 0.69 1.10 0.91 1.22 0.82

0853 LP1 16.1 7,127 1.06 0.77 1.18 1.56 0.64 1.61 0.62

0854 LP1 13 568 0.61 1.65 0.87 3.18 0.31 2.24 0.45

0855 SC5 04 116 0.63 1.52 0.93 0.95 1.06 1.12 0.89

0856 SC5 04 91 0.75 1.37 0.92 0.94 1.07 1.20 0.84

0857 SC5 06 177 0.94 1.29 0.83 0.92 1.08 1.09 0.91

0858 RES 01 45 1.66 0.84 0.70 1.04 0.96 1.49 0.67

0859 SC1 N/A - NaN NaN NaN NaN NaN NaN NaN

0860 SC5 06 217 0.81 1.38 0.88 0.92 1.08 1.08 0.92

0861 SC1 04 25 0.80 1.68 0.71 0.99 1.01 1.12 0.89

0862 SC5 06 103 0.60 1.57 0.92 0.97 1.03 1.01 0.99

0863 LP 13 2,529 0.87 1.31 0.86 1.40 0.71 1.95 0.51

0864 LP 10 4,006 1.34 0.87 0.88 1.26 0.80 1.36 0.74

0865 LP 7.1 3,346 0.97 1.10 0.94 1.07 0.93 1.66 0.60

0866 LP1 16.1 4,047 1.06 0.85 1.08 1.72 0.58 1.42 0.71

0867 SC4 10 178 1.20 0.90 0.94 1.68 0.59 2.05 0.49

0868 RES 07 15 0.93 1.98 0.46 1.09 0.92 1.11 0.90

0869 RES 08 347 1.18 1.02 0.84 0.87 1.15 0.90 1.11

0870 RES 01 49 1.31 0.93 0.84 0.95 1.05 1.31 0.76

0871 RES 01 48 1.94 0.63 0.75 0.93 1.08 1.48 0.68

0872 RES 01 56 1.82 0.60 0.83 1.06 0.95 1.32 0.76

0873 LP 10 2,049 1.64 0.63 0.90 1.31 0.76 1.26 0.79

0874 LP 9.1 14,147 1.16 0.82 1.03 1.09 0.92 1.05 0.95

0875 SC 10 276 1.39 0.83 0.88 2.12 0.47 1.70 0.59

0876 SC4 10 116 1.33 0.84 0.91 1.44 0.69 1.41 0.71

0877 LP 7.1 2,606 1.11 0.96 0.94 1.03 0.97 1.45 0.69

0878 RES 07 24 1.99 0.81 0.65 1.00 1.00 1.12 0.89

0879 LP 09 3,015 1.17 1.09 0.80 1.00 1.00 1.03 0.98

0880 RES 01 16 2.04 0.71 0.64 0.98 1.02 1.84 0.54

0881 RES 06 63 0.89 1.40 0.79 0.96 1.04 1.01 0.99

0882 LP 13 97 0.73 1.87 0.67 1.26 0.79 1.78 0.56

0883 LP1 01 2,265 2.20 0.54 0.70 1.09 0.92 1.43 0.70

0884 RES 07 49 0.89 1.48 0.74 0.92 1.08 1.25 0.80

0885 RES 04 77 0.87 1.53 0.73 0.91 1.10 1.22 0.82

0886 RES 05 54 0.80 1.72 0.68 0.97 1.04 0.86 1.16

0887 RES 05 128 0.56 2.02 0.78 0.97 1.04 0.85 1.18

0888 SC2 12 112 1.28 0.98 0.82 0.69 1.44 1.07 0.94

0889 LP 10 252 1.75 0.64 0.85 1.74 0.57 1.95 0.51


57


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0890 RES 07 18 1.58 0.99 0.66 0.91 1.10 1.67 0.60

0891 RES 07 78 1.48 0.92 0.74 0.99 1.01 1.51 0.66

0892 LP 10 8,196 1.20 0.86 0.97 1.15 0.87 1.18 0.85

0893 SC4 13 30 0.65 1.93 0.70 0.84 1.19 1.45 0.69

0894 RES 06 46 0.23 3.08 0.70 0.99 1.01 1.06 0.95

0895 SC 01 99 2.39 0.58 0.63 1.00 1.00 1.29 0.78

0896 LP 01 4,671 1.25 0.85 0.94 0.97 1.03 1.22 0.82

0897 RES 07 54 1.55 0.91 0.73 0.89 1.12 1.42 0.70

0898 RES 07 62 1.16 1.30 0.68 1.00 1.00 1.40 0.71

0899 RES 07 59 1.28 1.19 0.68 0.97 1.03 1.39 0.72

0900 RES 01 45 2.37 0.54 0.66 1.02 0.98 1.40 0.71

0901 RES 07 42 1.20 1.47 0.56 0.91 1.10 1.10 0.91

0902 RES 01 713 1.37 0.86 0.88 0.92 1.09 1.40 0.71

0903 LP1 9.1 1,581 1.07 1.03 0.91 1.02 0.98 0.94 1.07

0904 LP 01 7,945 1.18 0.87 0.97 1.01 0.99 1.17 0.86

0905 LP 01 931 4.04 0.36 0.46 1.04 0.96 1.38 0.73

0906 LP 01 2,257 1.29 0.79 0.97 1.07 0.93 2.02 0.49

0907 RES 01 47 2.49 0.50 0.66 0.99 1.01 1.95 0.51

0908 LP 07 449 1.19 1.18 0.73 0.89 1.13 1.72 0.58

0909 LP 01 11,209 1.23 0.85 0.95 1.03 0.98 1.19 0.84

0910 SC 05 19 0.81 1.24 0.97 1.00 1.00 0.30 3.31

0911 SC 05 34 0.72 1.42 0.93 1.00 1.00 0.30 3.33

0912 RES 01 21 1.82 0.75 0.74 0.91 1.10 1.36 0.73

0913 LP 04 2,565 1.05 1.10 0.88 0.87 1.15 1.34 0.75

0914 RES 04 30 0.91 1.34 0.83 1.00 1.00 1.26 0.79

0915 RES 04 27 0.87 1.24 0.90 0.92 1.09 1.15 0.87

0916 RES 03 36 2.46 0.47 0.76 0.97 1.03 1.09 0.92

0917 RES 01 48 1.25 0.95 0.86 0.99 1.01 1.18 0.85

0918 1A1 07 22 1.20 1.03 0.85 1.00 1.00 1.52 0.66

0919 SC4 01 80 1.91 0.69 0.74 1.12 0.89 5.36 0.19

0920 RES 06 128 0.98 1.24 0.83 0.98 1.02 1.07 0.94

0921 RES 06 56 0.54 2.34 0.64 1.00 1.00 1.08 0.93

0922 LP 02 854 1.21 0.89 0.94 0.96 1.05 0.71 1.40

0923 RES 01 38 2.16 0.67 0.63 1.03 0.97 2.10 0.48

0924 RES 01 90 1.44 0.89 0.78 0.92 1.09 1.48 0.68

0925 RES 01 51 1.63 0.78 0.76 1.00 1.00 1.16 0.86

0926 LP 01 4,936 1.26 0.85 0.94 1.08 0.92 2.84 0.35

0927 UNK 15 45 0.12 5.63 0.44 0.79 1.26 0.95 1.06

0928 LP 13 1,394 0.79 1.58 0.80 1.31 0.76 1.39 0.72

0929 RES 07 113 1.25 1.07 0.76 0.99 1.01 1.37 0.73


58


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0930 RES 01 40 2.16 0.61 0.73 0.97 1.03 1.38 0.72

0931 RES 16 75 1.13 1.04 0.86 0.84 1.20 1.18 0.85

0932 SC 02 2 1.16 1.01 0.87 1.00 1.00 0.64 1.56

0933 SC4 16.1 77 1.09 0.92 0.99 1.52 0.66 4.34 0.23

0934 RES 02 38 1.42 0.80 0.87 1.04 0.96 0.90 1.11

0935 RES 07 69 0.80 1.88 0.62 0.98 1.02 1.14 0.88

0936 RES 01 47 1.73 0.61 0.88 0.97 1.03 1.29 0.77

0937 RES 07 47 1.03 1.30 0.76 0.99 1.01 1.22 0.82

0938 LP 7.1 12,118 1.15 0.88 0.98 1.01 0.99 1.24 0.81

0939 LP 7.1 12,741 1.13 0.89 0.99 1.01 0.99 1.18 0.85

0940 LP 7.1 6,951 1.07 1.02 0.92 1.01 0.99 1.31 0.76

0941 LP 01 9,042 1.22 0.87 0.95 1.01 1.00 1.18 0.85

0942 LP 01 6,279 1.32 0.80 0.95 0.98 1.02 1.44 0.69

0943 LP 01 9,816 1.16 0.90 0.97 1.00 1.00 1.21 0.82

0944 RES 01 29 1.82 0.67 0.77 1.01 0.99 1.50 0.67

0945 LP 03 4,460 1.38 0.86 0.83 1.03 0.97 1.01 0.99

0946 LP5 01 452 2.70 0.54 0.61 0.98 1.02 1.27 0.79

0947 LP 13 98 0.65 2.05 0.69 0.82 1.22 2.56 0.39

0948 LP1 10 5,758 1.22 0.93 0.89 1.57 0.64 1.74 0.57

0949 ES1 16.1 4,867 0.67 1.25 1.12 2.08 0.48 2.11 0.48

0950 RES 08 91 0.76 1.87 0.62 0.90 1.11 0.90 1.11

0951 RES 01 53 1.57 0.75 0.83 0.97 1.03 1.28 0.78

0952 SC4 13 103 0.57 2.20 0.67 1.37 0.73 1.49 0.67

0953 SC1 05 141 0.43 2.49 0.68 1.01 0.99 0.89 1.13

0954 RES 01 19 1.81 0.56 0.93 1.13 0.88 1.31 0.77

0955 RES 16 31 1.93 0.81 0.61 0.81 1.24 1.24 0.80

0956 RES 07 38 1.08 1.33 0.71 1.02 0.98 2.34 0.43

0957 RES 01 34 1.71 0.79 0.75 0.91 1.10 2.01 0.50

0958 RT0 07 23 1.00 1.41 0.74 1.01 0.99 1.36 0.74

0959 1A1 10 22 1.62 0.79 0.82 0.66 1.52 1.47 0.68

0960 RES 04 51 0.63 1.97 0.70 0.97 1.04 1.14 0.88

0961 RES 01 52 1.57 0.81 0.77 1.02 0.98 1.23 0.81

0962 RT0 05 17 0.80 1.30 0.97 1.05 0.95 0.74 1.36

0963 RES 07 75 1.09 1.38 0.67 0.95 1.05 1.23 0.82

0964 RES 07 55 1.13 1.50 0.59 0.91 1.10 1.27 0.79

0965 RES 08 28 0.97 1.54 0.65 0.97 1.03 0.89 1.12

0966 SC 05 30 0.79 1.21 1.00 0.99 1.01 0.16 6.38

0967 LP 10 1,841 1.25 0.74 1.02 3.49 0.29 1.67 0.60

0968 RES 01 42 1.69 0.83 0.73 1.04 0.96 1.67 0.60

0969 RES 07 15 1.95 0.80 0.63 0.87 1.14 1.18 0.84


59


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

0970 RES 10 55 1.68 0.81 0.75 0.85 1.18 1.17 0.86

0971 RES 01 50 1.61 0.86 0.73 1.02 0.98 1.26 0.79

0972 SC 01 59 1.48 0.70 0.96 0.89 1.12 1.78 0.56

0973 RES 18 36 0.96 1.56 0.66 0.86 1.16 1.01 0.99

0974 LP1 16.1 4,344 0.97 1.02 1.01 2.11 0.47 2.41 0.41

0975 ES1 16.1 2,628 0.92 0.92 1.15 2.71 0.37 2.41 0.42

0976 RES 06 19 0.96 1.23 0.85 1.07 0.94 0.99 1.01

0977 RES 10 30 1.80 0.74 0.74 0.79 1.26 1.34 0.75

0978 UNK 07 628 1.12 1.36 0.66 1.05 0.96 2.20 0.45

0979 RES 01 39 2.25 0.59 0.66 0.98 1.02 1.31 0.76

0980 RES 10 37 2.12 0.59 0.76 0.82 1.21 1.60 0.63

0981 LP1 16.1 7,417 1.09 0.74 1.19 1.49 0.67 1.60 0.63

0982 LP 03 3,221 1.16 0.94 0.92 0.97 1.03 1.01 0.99

0983 SC 03 56 1.24 0.86 0.95 1.00 1.00 1.01 0.99

0984 RES 10 18 2.01 0.53 0.82 0.76 1.31 1.69 0.59

0985 RES 01 417 1.75 0.67 0.81 0.97 1.03 1.63 0.61

0986 RES 7.1 2,815 1.17 0.85 1.00 1.10 0.91 1.49 0.67

0987 RES 07 1,296 1.07 1.10 0.86 1.06 0.95 1.43 0.70

0988 RES 16 111 1.23 1.47 0.55 0.71 1.41 1.41 0.71

0989 LP5 16.1 2,660 1.19 0.76 1.09 3.37 0.30 2.39 0.42

0990 RES 13 112 0.72 2.05 0.61 0.72 1.39 1.41 0.71

0991 RES 07 61 1.59 0.87 0.71 0.90 1.11 1.50 0.67

0992 RES 04 75 0.60 2.33 0.60 0.94 1.06 1.84 0.54

0993 RES 04 55 0.74 1.97 0.62 0.93 1.08 1.66 0.60

0994 RES 07 77 1.42 1.02 0.70 1.02 0.98 1.56 0.64

0995 RES 01 57 1.86 0.66 0.80 1.08 0.92 1.67 0.60

0996 LP1 16.1 5,884 0.95 0.91 1.13 2.56 0.39 2.69 0.37

0997 RES 18 65 0.96 1.64 0.63 0.80 1.25 0.99 1.01

0998 LP1 16.1 16,539 1.09 0.79 1.13 1.61 0.62 1.87 0.54

0999 LP 04 367 0.70 1.69 0.81 1.02 0.98 1.13 0.88

1000 SC 05 11 0.53 1.40 1.12 1.00 1.00 0.44 2.25

1001 RES 07 30 0.98 1.36 0.76 0.92 1.09 1.33 0.75

1002 RT0 06 16 0.97 1.23 0.88 0.91 1.10 1.08 0.93

1003 RES 08 88 1.14 1.27 0.70 0.98 1.02 0.68 1.48

1004 LP 09 3,351 1.12 1.25 0.73 1.01 0.99 1.01 0.99

1005 LP 07 2,646 1.11 1.15 0.80 1.01 0.99 1.27 0.79

1006 LP 7.1 4,047 1.01 1.07 0.93 1.13 0.88 2.16 0.46

1007 LP 10 3,846 1.40 0.83 0.86 1.26 0.79 2.03 0.49

1008 RES 04 59 0.93 1.37 0.78 0.92 1.08 1.26 0.79

1009 LP1 16.1 2,267 1.10 0.83 1.08 2.02 0.49 2.09 0.48


60


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

1010 SC4 05 334 0.76 1.37 0.94 1.00 1.00 0.19 5.30

1011 SC 05 320 0.96 1.14 0.92 1.01 0.99 0.82 1.21

1012 SC 05 12 0.68 1.38 1.04 1.00 1.00 0.49 2.03

1013 SC 05 32 0.79 1.20 1.02 1.00 1.00 0.55 1.82

1014 RES 16 14 1.80 0.87 0.67 0.78 1.28 1.10 0.91

1015 RES 7.1 127 0.95 0.89 1.15 0.89 1.12 2.31 0.43

1016 LP 10 4,019 1.53 0.55 1.05 3.33 0.30 2.38 0.42

1017 RES 01 32 1.81 0.60 0.84 0.99 1.01 1.42 0.70

1018 LP 01 1,516 1.42 0.74 0.93 1.09 0.92 1.77 0.57

1019 RES 10 125 2.86 0.42 0.63 0.75 1.34 1.66 0.60

1020 RES 01 38 2.48 0.49 0.68 0.97 1.03 1.46 0.68

1021 RES 04 115 0.32 3.23 0.64 0.99 1.02 1.14 0.88

1022 RES 01 41 1.57 0.73 0.87 0.97 1.03 1.39 0.72

1023 LP 01 6,543 1.23 0.88 0.94 1.03 0.97 1.54 0.65

1024 LP 01 3,014 1.23 0.85 0.95 1.04 0.96 1.57 0.64

1025 LP 01 4,473 1.18 0.98 0.88 1.11 0.90 1.11 0.90

1026 LP 03 4,133 1.41 0.75 0.94 1.02 0.98 1.02 0.98

1027 SC4 16.1 172 1.02 0.97 1.01 1.69 0.59 2.63 0.38

1028 SC4 01 115 1.23 0.85 0.95 0.92 1.08 2.42 0.41

1029 RES 04 65 0.62 1.64 0.87 1.02 0.98 1.54 0.65

1030 LP 03 5,496 1.34 0.81 0.91 0.99 1.01 0.98 1.02

1031 RES 04 58 0.64 2.11 0.64 0.97 1.03 1.35 0.74

1032 LP 10 409 1.32 0.84 0.91 1.86 0.54 1.66 0.60

1033 RES 01 48 1.45 0.76 0.92 0.94 1.06 1.45 0.69

1034 LP 01 568 1.58 0.64 0.96 1.01 0.99 1.12 0.89

1035 LP 01 510 1.33 0.94 0.82 1.00 1.00 1.33 0.75

1036 RES 07 42 0.97 1.47 0.69 0.88 1.13 1.31 0.76

1037 IS1 8.1 331 1.06 1.04 0.91 0.99 1.01 0.49 2.04

1038 SC2 06 3 0.58 2.32 0.58 0.92 1.09 1.06 0.94

1039 RES 01 4 1.38 0.60 0.91 1.05 0.95 1.67 0.60

1040 LP 01 362 1.76 0.67 0.80 1.11 0.90 2.42 0.41

1041 LP 10 7,773 1.13 0.98 0.92 1.43 0.70 1.80 0.56

1042 LP 9.1 4,365 0.87 1.11 1.04 1.07 0.94 1.06 0.94

1043 LP 9.1 15,040 1.00 0.97 1.03 0.94 1.06 1.02 0.98

1044 LP 10 6,585 1.15 0.94 0.94 1.32 0.76 1.51 0.66

1045 LP 05 2,537 0.89 1.24 0.91 1.02 0.98 0.61 1.64

1046 RES 04 84 0.87 1.54 0.73 0.93 1.07 1.89 0.53

1047 RES 06 35 0.81 1.59 0.74 0.92 1.09 1.09 0.92

1048 RES 09 78 0.95 1.51 0.70 0.93 1.08 1.04 0.96

1049 LP 01 276 1.70 0.64 0.89 1.07 0.94 1.66 0.60


61


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

1050 RES 04 49 0.86 1.55 0.73 1.03 0.97 1.38 0.72

1051 RES 05 2 0.90 1.07 1.01 1.00 1.00 0.55 1.81

1052 RES 01 49 1.14 1.03 0.87 0.95 1.05 1.67 0.60

1053 SC4 07 304 1.00 1.35 0.74 0.93 1.08 1.32 0.76

1054 LP1 16.1 2,645 1.19 0.79 1.04 2.12 0.47 1.74 0.57

1055 SC 01 281 1.80 0.61 0.87 1.10 0.91 1.34 0.75

1056 SC5 06 98 0.75 1.35 0.93 0.96 1.05 1.06 0.94

1057 LP1 04 4,522 0.80 1.25 0.98 0.96 1.04 1.16 0.86

1058 UNK 7.1 4,656 1.06 0.95 0.99 0.98 1.02 1.16 0.86

1059 LP1 01 14,957 1.10 1.03 0.89 1.01 0.99 1.13 0.89

1060 SC4 01 288 1.34 0.74 0.99 1.00 1.00 1.12 0.90

1061 LP 01 164 1.31 0.84 0.91 0.99 1.01 1.21 0.83

1062 SC6 10 93 1.60 0.67 0.91 1.29 0.78 1.63 0.61

1063 LP 01 2,911 1.19 0.92 0.92 0.95 1.05 1.37 0.73

1064 SC2 05 352 0.36 2.65 0.76 1.08 0.92 0.86 1.17

1065 LP1 16.1 2,478 1.03 0.97 1.01 2.37 0.42 2.17 0.46

1066 RES 07 26 1.20 1.09 0.76 0.96 1.04 1.11 0.90

1067 RES 01 77 1.95 0.66 0.72 0.87 1.14 1.25 0.80

1068 SC2 03 1 1.39 0.71 0.99 1.03 0.97 0.96 1.04

1069 1A1 09 80 1.38 1.10 0.69 1.01 0.99 1.09 0.92

1070 LP 9.1 77,859 1.04 0.97 1.00 1.01 0.99 1.00 1.00

1071 SC 9.1 559 1.07 0.94 0.99 1.01 0.99 1.05 0.95

1072 SC 9.1 334 1.08 0.99 0.94 0.99 1.01 1.06 0.95

1073 SC 9.1 503 1.03 0.97 1.00 1.02 0.99 1.03 0.97

1074 SC4 01 123 1.45 0.62 1.04 1.07 0.94 1.48 0.68

1075 RES 01 54 1.79 0.73 0.73 0.99 1.01 1.47 0.68

1076 LP 13 542 0.71 1.70 0.80 1.34 0.75 1.26 0.79

1077 IS 16.1 18,792 1.06 0.83 1.11 3.97 0.25 3.94 0.25

1078 LP 16.1 983 0.86 1.17 0.99 2.22 0.45 2.50 0.40

1079 LP 16 149 0.97 1.66 0.60 1.18 0.85 1.24 0.80

1080 LP 06 1,339 0.37 2.70 0.72 1.08 0.93 1.03 0.97

1081 SC4 10 238 1.47 0.79 0.87 1.25 0.80 1.38 0.72

1082 RES 04 70 0.69 1.89 0.69 0.99 1.01 1.34 0.75

1083 RES 04 67 0.60 1.96 0.73 1.01 0.99 1.23 0.81

1084 LP 03 8,090 1.23 0.95 0.86 1.01 0.99 1.04 0.96

1085 LP 9.1 3,512 1.02 0.91 1.06 1.02 0.98 1.08 0.92

1086 LP 03 16,224 1.23 0.86 0.95 1.01 0.99 1.01 0.99

1087 SC 16 23 2.23 1.04 0.41 1.77 0.56 1.88 0.53

1088 UNK 09 79 1.29 1.13 0.70 0.98 1.03 1.04 0.96

1089 RES 01 30 2.50 0.52 0.64 0.94 1.06 1.27 0.79


62


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

1090 RT0 18 19 1.08 1.21 0.79 0.86 1.17 1.04 0.96

1091 LP1 16.1 5,270 1.05 0.88 1.07 2.18 0.46 1.97 0.51

1092 RES 05 104 0.57 2.81 0.48 0.95 1.05 0.81 1.24

1093 RES 01 71 1.53 0.83 0.80 0.94 1.07 1.19 0.84

1094 LP 07 1,565 1.20 1.01 0.83 1.02 0.98 1.59 0.63

1095 RES 07 37 1.25 1.06 0.77 0.88 1.14 1.19 0.84

1096 SC 05 24 0.79 1.23 1.02 1.00 1.00 0.26 3.81

1097 RES 09 62 1.34 1.24 0.61 1.00 1.00 0.98 1.03

1098 LP1 16.1 3,393 0.95 0.96 1.08 1.94 0.52 1.96 0.51

1099 LP1 03 5,091 1.19 0.89 0.94 1.05 0.95 0.94 1.06

1100 LP 13 5,384 0.99 1.20 0.85 1.40 0.71 1.33 0.75

1101 SC 10 214 1.61 0.68 0.90 1.36 0.74 1.91 0.52

1102 LP 01 441 1.32 0.77 0.96 1.02 0.98 2.25 0.44

1103 LP 10 1,912 1.18 0.91 0.93 3.15 0.32 2.74 0.37

1104 ES1 13 1,048 0.77 1.64 0.76 1.56 0.64 1.54 0.65

1105 ES1 13 3,953 0.62 1.46 1.02 3.29 0.30 3.12 0.32

1106 RES 02 45 1.82 0.67 0.77 0.87 1.15 0.81 1.24

1107 SC4 13 30 0.86 1.28 0.89 2.81 0.36 3.73 0.27

1108 RES 01 56 1.44 0.77 0.89 1.00 1.00 1.64 0.61

1109 RES 01 28 1.96 0.70 0.69 0.93 1.08 1.19 0.84

1110 RES 01 36 1.39 0.88 0.83 0.99 1.01 1.33 0.75

1111 RES 01 62 1.21 0.94 0.88 1.09 0.91 1.74 0.57

1112 RES 07 41 1.38 1.30 0.55 0.89 1.12 1.22 0.82

1113 RES 10 21 1.71 0.81 0.70 0.80 1.25 1.94 0.52

1114 SC4 13 245 0.73 1.46 0.88 1.19 0.84 1.17 0.85

1115 LP 13 817 0.54 2.32 0.65 1.21 0.82 1.23 0.81

1116 RES 13 26 0.60 1.72 0.88 1.18 0.85 1.61 0.62

1117 RES 04 90 0.98 1.25 0.82 0.98 1.02 1.32 0.76

1118 SC 15 108 0.64 2.00 0.69 1.32 0.76 1.06 0.95

1119 RES 07 43 1.58 0.87 0.72 0.97 1.03 1.56 0.64

1120 RES 15 43 0.68 1.95 0.67 0.82 1.23 1.04 0.96

1121 LP 16.1 4,897 0.94 1.05 1.02 2.13 0.47 1.11 0.90

1122 RES 07 107 1.29 1.18 0.67 1.01 0.99 1.16 0.86

1123 RES 07 275 1.41 1.12 0.67 1.14 0.88 1.29 0.77

1124 SC 05 21 0.95 1.01 1.03 1.00 1.00 0.72 1.39

1125 RES 03 25 2.79 0.49 0.62 0.98 1.02 1.06 0.94

1126 SC 08 59 0.94 1.38 0.78 1.00 1.00 0.72 1.40

1127 LP 7.1 127 0.99 1.06 0.96 1.01 0.99 1.98 0.50

1128 RES 01 34 2.44 0.54 0.64 0.94 1.06 1.36 0.74

1129 RES 09 61 1.46 0.97 0.73 0.99 1.01 1.01 0.99


63


Winter FLB

Shoulder FLB

Wk/ Day FLB

Wk/ End FLB

Day FLB

Night FLB

1130 LP 13 4,574 0.86 1.18 0.97 1.60 0.63 2.18 0.46

1131 LP 16.1 3,489 0.78 1.17 1.06 1.46 0.69 1.74 0.57

1132 RES 06 44 0.57 2.40 0.59 1.00 1.00 0.92 1.09

1133 RES 16 22 1.17 1.48 0.57 0.77 1.29 1.82 0.55

1134 ES1 16.1 4,747 0.91 1.10 0.98 1.67 0.60 2.16 0.46

1135 RES 10 45 1.89 0.77 0.69 0.78 1.28 1.49 0.67

1136 SC 05 29 0.93 1.07 1.00 0.99 1.01 0.27 3.70

1137 UNK 10 139 1.83 0.68 0.84 1.30 0.77 1.60 0.63

1138 LP1 01 937 1.25 0.83 0.97 1.07 0.94 1.11 0.90

1139 SC4 07 61 1.18 1.27 0.68 0.98 1.02 1.19 0.84

1140 RES 07 66 0.76 1.97 0.61 0.97 1.03 1.51 0.66

1141 RES 01 18 1.35 0.94 0.81 0.94 1.07 1.63 0.61

1142 RES 10 541 1.62 0.81 0.75 0.64 1.56 1.53 0.65

1143 SC4 01 199 1.54 0.79 0.84 0.91 1.10 1.19 0.84

1144 RES 03 48 1.56 0.78 0.80 0.94 1.06 1.08 0.92

1145 RES 01 32 2.45 0.51 0.70 0.93 1.08 1.37 0.73

1146 RES 09 250 0.94 1.43 0.74 0.94 1.07 1.03 0.97

1147 LP 10 575 1.31 0.85 0.87 2.80 0.36 3.30 0.30

1148 LP 01 253 1.47 0.71 0.92 0.97 1.03 2.12 0.47

1149 RES 09 46 0.94 1.72 0.59 0.98 1.02 1.06 0.95

1150 RES 01 26 1.34 0.82 0.90 1.02 0.98 1.45 0.69

Northern Virginia Electric Cooperative (NOVEC)€¦ · • Extremely High volume customers (aka...

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