Clusturing Artificial

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Abstract-- A novel clustering based Short Term LoadForecasting (STLF) using Artificial Neural Network (ANN) to

forecast the 48 half hourly loads for next day is presented in this

paper. The proposed architecture uses the historical load and

temperature to forecast the next day load. It is trained using back

propagation algorithm and tested. The daily average load of each

day for all the training patterns and testing patterns is calculated

and the patterns are clustered using a threshold value between

the daily average load of the testing pattern and the daily average

load of the training patterns. The results obtained from neural

network are presented and the results show that the clusteringbased approach is more accurate.

Index Terms-- Artificial Neural Network, Back Propagation

Algorithm, Clustering, Short Term Load Forecasting.

I. INTRODUCTION

LECTRICAL energy is a superior form of energy for all

types of consumer needs. The close tracking of the

system load by the system generation at all times is the basic

requirement in the operation of power systems [1]. There is a

3-7% increase of electric load per year for many years. The

increase of load depends on the population growth, local area

development, industrial expansion etc. The taxonomy of loadforecasting can be considered as Spatial forecasting &

Temporal forecasting. Forecasting future load distribution in a

particular region, such as a county, a state, or the whole

country is called Spatial forecasting. Temporal forecasting is

dealing with forecasting load for a specific supplier or

collection of consumers in future hours, days, months, or even

years. The temporal forecasting can be broadly divided into 4

types long term, medium term, short term and very short

term. The long term forecast (5 to 20 years) is required as the

building of power plant requires many years. The forecast

ranging from few months to 5 years is termed as medium term

forecasting. Thus long and medium term forecasts help indetermining the capacity of generation, transmission or

distribution system expansions and the type of facilities

required in transmission expansion planning, annual hydro

thermal maintenance scheduling etc. Typically the short term

Amit Jain is with Power Systems Research Center, International Institute of

Information Technology, Hyderabad, Andhra Pradesh, India. (e-mail: amit

@iiit.ac.in).

B. Satish is with Power Systems Research Center, International Institute of

Information Technology, Hyderabad, Andhra Pradesh, India. (e-mail: satish_b

@research.iiit.ac.in).

load forecast covers a period of one week. The forecast

calculates the estimated load for each hour of the day, the

daily peak load and the daily/weekly energy generation. The

forecasted data is used for:

Unit commitment (Selection of generators in operation,start up/shut down of generation to minimize operation

cost)

Hydro scheduling to optimize water release fromreservoirs

Hydro-Thermal co-ordination to determine the least costoperation mode (optimum mix)

Interchange scheduling & energy purchaseTransmission line loadingPower system security assessment (load flow & transient

stability studies)

These offline network studies detect conditions under which

the system is vulnerable and warn for corrective actions like

load shedding, power purchase, starting up of peak units,

switching off interconnections and increasing spinning and

stand by reserve. Hence, the day to day operation of the

power system requires accurate short term load forecasting.

Bunn [2] reported that 1% increase in the forecasting error

leads to an increase of 10 million operating cost per year.The purpose of very short term load forecasting (ranging from

minutes to hours) is for real time control & security

evaluation [3].

The introduction of deregulation in the electricity industry

made short term load forecasting much more important.

Because of its great economic importance and the high

complexity of electric power systems, short term load

forecasting has been subjected to constant improvements in

which numerous techniques have been used [1, 2, 4].

Different techniques for load forecasting: Time series

models (load is modeled as a function of its past observed

values), multiplicative auto-regressive models [5], dynamiclinear [6] or non-linear models [7], threshold auto-regressive

models [8], methods based on Kalman-filtering [9, 10, 11],

Box Jenkins transfer functions [12, 13], ARMAX models

[14, 15], optimization techniques [16], non-parametric

regression [17], structural models [18] and curve fitting [19]

procedures. The most popular ones are linear regression ones

[20, 21, 22, 23, 24]. Artificial Intelligence techniques include

Expert Systems [25, 26], Fuzzy inference [27] and Fuzzy

neural models [7, 28].

In this paper, an attempt is being made to predict the next

Clustering based Short Term Load Forecasting

using Artificial Neural Network

Amit Jain,Member, IEEE, and B. Satish

E

978-1-4244-3811-2/09/$25.00 2009 IEEE

orized licensed use limited to: INTERNATIONAL INSTITUTE OF INFORMATION TECHNOLOGY. Downloaded on October 14, 2009 at 08:38 from IEEE Xplore. Restrictions apply.


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day load by using artificial neural network by clustering the

training patterns with respect to the testing pattern. The paper

is organized as follows: Section II discusses the basics of

ANN and past literature on ANN for STLF. Section III

explains the proposed architecture and its description. The

solution methodology and the results are presented in Section

IV and Section V respectively. Section VI contain the

conclusions & the future work.

II. BASICS OF ANN AND LITERATURE ON ANN FOR STLF

Among the Artificial Intelligence techniques available,

ANN is widely used for forecasting the electric load. ANN

can be defined as highly connected array of elementary

processors called neurons and is capable to perform non-

linear modeling and adaptation. Neural networks attempt to

learn by themselves the functional relationship between

system inputs and outputs.

In case of load forecasting, it uses previous load patterns as

in the cases of Time series and Regression approaches and

weather information as in the case of Regression approach;

thus ANN has advantages of both of Time series andRegression methods. The feed forward back propagation

algorithm, which updates the weights in such a way that the

error is minimized, is used to train the neural networks. The

detailed explanation of back propagation algorithm is

available in any standard neural network textbook.

Park et al. [29] presented an ANN approach to electric load

forecasting in which the ANN is trained with the back

propagation algorithm. Peng et al [30] proposed a procedure

for choosing the training cases, which are most similar to the

forecasted inputs. Khotanzad et al [31] presented a load

forecasting system known as ANNSTLF, which predicts the

next 24 hours load. It includes two ANN forecasters. One ofthem predicts the base load and the other forecasts the change

in load. The final forecast is computed by an adaptive

combination of these two forecasts. The effect of humidity

and wind speed is considered through a linear transformation

of temperature. Till date, several researchers dealt with the

application of various neural networks to Short Term Load

Forecasting with varying success [32-41]. Although neural

networks are capable of handling nonlinearity between the

electric load and the weather factors that affect the load, they

somehow lack to fully handle unusual changes that occur in

the environment. The topology of a neural network

determines the degrees of freedom available to model the

data. If the neural network is too simple then the network will

not be able to learn the function relating the input to the

output and an over-complex network will learn the noise in

the data and will not be able to generalize.

III. PROPOSED ARCHITECTURE

A model is proposed here to forecast the electric load one

day in advance. The aim is to prepare the model for real-time

forecasts by clustering the available past data.

The systems electric load, which is the sum of the

individual loads, has two components: Base component and

Random component. These components vary in the load curve

due to the following factors:

Economical or EnvironmentalTimeWeather andRandom disturbances

The economical/environmental factors include change in

Service area demographics (rural, residential), Industrial

growth, Emergence of new industry, Change of farming,

Penetration or saturation of appliance usage, Economical

trends (Expansion or Recession), Change of the price of

electricity and Demand side load management. The time

constraints of economical/environmental factors are slow,

measured in years. The time factors affecting the load are

seasonal variation of load (Summer, Winter etc.), start of

school year etc. This also include weekly cyclic variation like

significant reduction in load on weekends like Saturday &

Sunday, slight reduction in load on Monday & Friday, similar

pattern of load on the other days of the week and different

pattern of load on holidays like Christmas, New Year,Vacation etc. The various weather factors that affect the load

are air temperature, dew temperature, wet bulb temperature,

relative humidity, thunderstorms, wind speed, rain, fog, snow,

cloud cover/sunshine. Not all weather factors are similar in

importance and among them temperature is the most

important as it has direct influence on many kind of electrical

consumption. The random disturbances include start or stop

of large loads (steel mill, factory or furnace), widespread

strikes, sporting events (football games, cricket matches etc.),

popular television shows and shut-down of industrial facility.

The proposed architecture is shown is Fig. 1. The objective

of the proposed architecture is to recognize the above factorsfrom the training data and predict the load accordingly. Thus

a suitable architecture along with appropriate inputs is

needed. There are no general rules to follow in the selection

of input variables. It depends largely on experience,

professional judgment and preliminary experimentation. The

demand for electricity is known to vary by the time of the day,

week, month, temperature and usage habits of the consumers.

Though usage habit is not directly observable, it may be

implied in the patterns of usage that have occurred in the past.

For solving a STLF problem all of these inputs are not needed

at the same time. Depending on the forecast to be made,

whether daily or hourly; the choice of input variables will

change.

Fig. 1 Proposed architecture for STLF

Neural NetworkTd-1

Td

Ld-1

DOW

Ld



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A. Description of Proposed Architecture

INPUTS:53Load (Ld-1): 48 half-an-hour loads

Temperature (Td-1): 1 (average temperature)

Forecasted days average Temperatures (Td):1

Day Of the Week (DOW) to be forecasted: 3

(Sunday-001, Monday-010, Tuesday-011, Wednesday100,

Thursday-101, Friday-110, Saturday-111)

OUTPUTS:48Forecasted Load (Ld): 48 half-an-hour loads

IV. SOLUTION METHODOLOGY

A. Data Analysis

The data considered for the proposed architecture include

electricity load and temperature. The load data set contains

the load per half hour of each day for two consecutive years

while the temperature data set provides the average daily

temperature for the same two consecutive years. The 2 years

data contains 104 daily load curves for each day of a week.

The data is divided into 2 sets with 91 patterns for training

and 13 patterns for testing for each day of the week.

Fig. 2 and Fig. 3 represent the daily load curves of the

training set for Sunday and Wednesday, representing a

weekend day and a weekday, respectively. They clearly show

that the load is changing with season and furthermore the load

pattern of weekdays is different from that of the weekend.

Fig. 2 Training patterns for Sunday

Fig. 3 Training patterns for Wednesday

Fig. 4 Clustering of training patterns for different testing patterns for Tuesday

--- Training

patterns

*-- Testing

pattern



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B. Creating the Sample Set

The ANN is trained with the historic data before testing

them. The first step for training them is obtaining an accurate

historical data. The data should be chosen that is relevant to

the model. How well the data is chosen is the defining factor

in how well the networks output will match the event being

modeled. There should be some correlation between the

training data and the testing data. In the load data, in generalall the Sundays load data look alike, all the Mondays data

look alike and this holds good for all the days of the week.

Hence for testing a day, the training data considered is the

past data same as that of the testing day.

C. Data Preparation

In this stage, the typical (raw) input data has to be arranged

as input and output pattern pairs for training the ANN. The 53

inputs for the ANN to be arranged as one column vector and

the 48 outputs are to be arranged as another column vector.

This is to be done for all the days of the past data.

D. NormalizationNormalization is an important stage for training the neural

network. The data is normalized in such a way that the higher

values should not suppress the lower values in order to retain

the activation function [42]. Both the load and the

temperature data should be normalized to the same range of

values.

E. Clustering

The daily average load is calculated for all the 104 patterns

for all the days of the week. The patterns are clustered based

on the threshold value of the difference between the daily

average load of the training patterns and the daily average

load of the testing pattern.

TABLEI

PATTERNS CLUSTER FOR ALL THE 13 TESTING PATTERNS FOR TUESDAY

Serial

No.

Testing pattern

Number

No. of Training patterns

matched

1 1 7

2 2 7

3 3 11

4 4 23

5 5 13

6 6 257 7 22

8 8 18

9 9 17

10 10 11

11 11 9

12 12 18

13 13 22

Fig. 4 shows the clustered training patterns for all the 13

testing patterns by considering a threshold value of 30MW for

Tuesday. TABLE I contains the patterns matched for each

Testing pattern for Tuesday corresponding to Fig. 4.

V. RESULTS

A. Without clustering

The neural network is trained initially with thecorresponding day patterns without doing clustering. Hence

for each day, the ANN is trained with 91 patterns.

B. With clustering

In this step, the training patterns are clustered by using a

threshold value of 30MW between the daily average load of

the testing pattern and the daily average load of the training

patterns for all the days.

TABLE II shows the comparison of maximum and average

percentage errors of ANN (without clustering & with

clustering) for 5th

Testing pattern for all the days. The results

show that the maximum and average % errors are less for all

the days when the training patterns are clustered.

TABLEII

COMPARISONOFMAXIMUM&AVERAGE%ERRORSOFANN

(WITHOUTCLUSTERING&WITHCLUSTERING) FOR5THTESTING

PATTERN

Day % Error

ANN

(Without

Clustering)

ANN

(With

Clustering)

SundayMax. % Error 16.5899 8.4659

Avg. % Error 10.6365 3.3788

MondayMax. % Error 16.0491 11.6849

Avg. % Error 7.8471 4.3927Tuesday

Max. % Error 21.0562 14.5678

Avg. % Error 12.0532 6.4338

WednesdayMax. % Error 21.7866 8.3199

Avg. % Error 13.1411 3.6995

ThursdayMax.% Error 18.1500 12.9011

Avg. % Error 11.6557 3.9104

FridayMax. % Error 20.1283 15.4229

Avg. % Error 11.4397 3.4454

SaturdayMax. % Error 10.0836 9.0649

Avg. % Error 3.7211 3.3598

TABLEIII

PATTERNS CLUSTER FOR 5TH TESTING PATTERN FOR DIFFERENT THRESHOLDS

DayPatterns Matched

(30 MW Threshold)

Patterns Matched

(80 MW Threshold)

Sunday 16 45

Monday 21 45

Tuesday 13 38

Wednesday 20 41

Thursday 19 40

Friday 20 40

Saturday 18 42



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The patterns are also clustered by considering 80MW

threshold. The no. of patterns matched for each day for the 5th

testing pattern is shown in TABLE III. From this table, we can

observe that the more the threshold, the more the number of

patterns matched.

TABLEIV

COMPARISONOFMAXIMUM&AVERAGE%ERRORSOFANN(WITH

CLUSTERINGFOR30MW&80MW)FOR5THTESTINGPATTERN

Day % Error

ANN (30MW

Threshold)

(With

Clustering)

ANN (80MW

Threshold)

(With

Clustering)

SundayMax. % Error 8.4659 10.3134

Avg. % Error 3.3788 3.4542

MondayMax. % Error 11.6849 12.9257

Avg. % Error 4.3927 5.3571

TuesdayMax. % Error 14.5678 16.7703

Avg. % Error 6.4338 8.6646

WednesdayMax. % Error 8.3199 9.5505

Avg. % Error 3.6995 4.5594

ThursdayMax.% Error 12.9011 14.6886

Avg. % Error 3.9104 5.5892

FridayMax. % Error 15.4229 16.8100

Avg. % Error 3.4454 3.6120

SaturdayMax. % Error 9.0649 9.1913

Avg. % Error 3. 3598 3.6568

TABLE IV shows the comparison of maximum and

average percentage errors of ANN (with clustering for 30MW

& 80MW threshold values) for 5th

Testing pattern for all the

days. The results show that the maximum and average %

errors are less for all the days for 30MW threshold whencompared to the 80MW threshold. It also shows that

maximum and average % errors are less for 80MW threshold

compared to the errors when patterns are not clustered (refer

TABLE II for errors without clustering).

Fig. 5 Comparison of Actual & Predicted loads from ANN for Sunday for 7 th

testing pattern

Fig. 5 and Fig. 6 shows the comparison of actual and

predicted loads from ANN (without & with clustering

30MW) for Sunday & Wednesday for 7th

testing pattern,

respectively. The predicted loads obtained by using clustering

approach are more accurate than the loads obtained without

clustering.

Fig. 6 Comparison of Actual & Predicted loads from ANN for Wednesday for

7th testing pattern

VI. CONCLUSIONS &FUTURE WORK

A clustering based neural network approach for predicting

the next day load is discussed. The training patterns for a

particular day are generated by using a threshold between the

daily average loads of the all training patterns and the daily

average load of the testing pattern. The predicted load

obtained by clustering the training patterns is following the

actual load closely compared to the predicted load obtained

without clustering. Results are presented for different

threshold values which result in forming different cluster

patterns. The data should be normalized before training the

ANN and the method of normalization also affect the error in

forecasting. The proposed method does not require any heavy

computational burden and can be easily implemented

compared to the conventional approaches. The most

important conclusion from the present work is to show the

applicability of clustering techniques for choosing trainingpatterns for ANN based methods for getting better short term

load forecasting results. By using Pattern Recognition

techniques, the patterns, similar to that of the input of the

testing pattern, can be chosen (instead of clustering based on

the daily average load corresponding to the day to be

forecasted) from the past data and then trained. This may be

better approach and may still reduce the forecasting error. The

authors are working on this direction and the results for that

study will be presented in a future publication.

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VIII. BIOGRAPHIES

Amit Jain graduated from KNIT, India in Electrical

Engineering. He completed his masters and Ph.D.

from Indian Institute of Technology, New Delhi,

India.

He was working in Alstom on the power SCADA

systems. He was working in Korea in 2002 as a

Post-doctoral researcher in the Brain Korea 21

project team of Chungbuk National University. He

was Post Doctoral Fellow of the Japan Society for

the Promotion of Science (JSPS) at Tohoku

University, Sendai, Japan. He also worked as a Post Doctoral Research

Associate at Tohoku University, Sendai, Japan. Currently he is an Assistant

Professor in IIIT, Hyderabad, India. His fields of research interest are power

system real time monitoring and control, artificial intelligence applications,power system economics and electricity markets, renewable energy, reliability

analysis, GIS applications, parallel processing and nanotechnology.

B. Satish is a Ph. D. candidate in Power Systems

Research Center, International Institute of Information

Technology, Hyderabad, India. He received his B. Tech

degree from Koneru Lakshmaiah College of Engineering,

Vijaywada, India and M. Tech degree form IIT Madras,

India. He worked for three and half years as a faculty in

Department of EEE, Vellore Institute of Technology,

Vellore and published 12 papers at International and

national levels including IEEE and ELSEVIER. His areas of interest include

Applications of Artificial Neural Networks and Fuzzy logic to power systems,



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