A Hybrid Short Term Load Forecasting Model of an Indian Grid

doi:10.4236/epe.2011.32024

Paper Menu >>

Journal Menu >>

Energy and Power En gi neering, 2011, 3, 190-193

doi:10.4236/epe.2011.32024 Published Online May 2011 (http://www.SciRP.org/journal/epe)

A Hybrid Short Term Load Forecasting Model of an

Indian Grid

Rabindra Behera1, Bibhu Prasad Panigrahi1, Bibhuti Bhusan Pati2

1Department of Electrical Engineering I. G. I. T. Sarang, Orissa, India

2Department of Electrical Engineering VSSUT Burla, Orissa, India

E-mail: [email protected], bibhu89@yahoo.com, [email protected]

Received March 21, 20 1 1; revised March 28, 2011; accepted April 8, 2011

Abstract

This paper describes an application of combined model of extrapolation and correlation techniques for short

term load forecasting of an Indian substation. Here effort has been given to improvise the accuracy of elec-

trical load forecasting considering the factors, past data of the load, respective weather condition and finan-

cial growth of the people. These factors are derived by curve fitting technique. Then simulation has been

conducted using MATLAB tools. Here it has been suggested that consideration of 20 years data for a devel-

oping country should be ignored as the development of a country is highly unpredictable. However, the im-

portance of the past data should not be ignored. Here, just previous five years data are used to determine the

above factors.

Keywords: Short Term Load Forecasting, Parameter Estimation, Trending Technique, Co-Relation

1. Introduction

Electrical energy is a superior form of energy for all

types of consumer needs. The close tracking of system

generation at all time is the basic requirement in the op-

eration of power system. There is a 3% - 7% of increase

of electrical load per year for many years. Short-term

load forecasting (STLF) is essential for an effective en-

ergy management in a deregulated power open market.

However, the electric power load forecasting problem is

not easy to handle due to nonlinear and random-like be-

haviors of system loads, weather conditions, and varia-

tions of social and economic environments.

A wide variety of models have been proposed in the

last two decades for STLF due to its importance etc. A

wide variety of models have been proposed in the last

two decades for STLF due to its importance, such as

Functional clustering and linear regression for peak load

forecasting [1], Mixed price and load forecasting of elec-

tricity markets by a new iterative prediction method [2]

and univariate modeling and forecasting of monthly en-

ergy demand time series using abductive and neural

networks [3] etc. Moreover, the electrical load forecast-

ing depends on many known and unknown variables.

These variables can be considered to have steady values

within a specified reg ion under one electricity regulatory

authority. This type of load forecasting is being termed

as spatial load forecasting.

Aldo Goia, Caterina May and Gianluca Fusai in their

paper “Functional clustering and linear regression for

peak load forecasting” suggested a new approach using

past heating demand data in a district-heating system.

Nima Amjadya, Ali Daraeepour in their paper “Mixed

price and load forecasting of electricity markets by a new

iterative prediction method” suggested real conditions of

an electricity market and short-term load forecasting. R.

E. Abdel-Aalin’s paper “Univariate modeling and Fore-

casting of Monthly Energy Demand Time Series Using

Abductive and Neural Networks” suggested univariate

modeling of the monthly demand time series based only

on data for 6 years to forecast the demand for the seventh

year contrary to multivariate models.

Here the load and weather data of Bhubaneswar (India)

power grid has been collected for six consecutive years.

Also the economic gr owth of the people is studied. It has

been observed that economic growth in short term can be

considered as neglig ible.

A Case study has been conducted on an Indian grid

located at Bhubaneswar, Orissa based on previous load

and weather data.

R. BERERA ET AL.

191

2. Proposed Model

This model is a combined model of extrapolation and

correlation techniques. Extrapolation techniques involve

fitting trend curves to basic historic data adjusted to re-

flect the growth trend itself. With a trend curve the fore-

cast is obtained by evaluating the trend curve function at

the desired future point. Correlation techniques of fore-

casting relate system loads to various demographic and

economic factors. This approach is advantageous in

forcing the forecaster to understand clearly the interrela-

tionship between load growth patterns and other meas-

urable factors. The advantage is the need to forecast

demographic and economic factors. Typically, the fac-

tors such as population, building permits, business,

weather data and the like are used in correlation tech-

niques [4-8].

No one method of forecasting is effective in all situa-

tions. Here, effort has been given by considering a par-

ticular location and temperature variation in the month of

January. A study has been conducted on the real-time

data collected over past five years. It may be suggested

that the load data can be divided into three parts: 1) con-

stant about 90%; 2) weather dependent about 5% - 6%; 3)

Unpredictable about 4% - 5%. Considering the above

facts we may estimate the constant term as follows:

[9-13]



yk bk

N





















(1)

 

111

NNN

kkk

Nykkkyk

bNk k























(2)

where, N = Total number of data.



load data at |th interval

yk k

average or mean value

y

The average weather report of metrological depart-

ment India has been suggested in their website. The

yearly temperature graph in the region is represented in

Figure 1;

The curve fitting weather correlation function may be

defined as:

 

1sin

yk AeBk (3)

where the constants A, B and a can be calculated based

upon the amplitude and respective instant.

Hence, the forecasting equation can be given as:

 

1ddd

ykyky k (4)

A computer programme can be developed to simulate

Figure 1. Statistical data of temperature vs days in a year.

the above equation. Here a matlab simulation is being

conducted.

3. Results and Discussion

A case study has been conducted on an Indian grid lo-

cated at Bhubaneswar using MATLAB Toolbox. The

details of the case study are mentioned in the following

tables and graphs. Here, the load data for five consecu-

tive years has been taken into consideration. Earlier

various models and simulation results suggested that

more and more real time data can help the forecaster to

obtain a more accurate result. However, here it is be-

lieved that data more than five years are obsolete. This

algorithm may be useful for two years, next we have to

check again the value of the constants, as they are all

time dependent. It is so because the financial growth of

the people, weather condition, advancement of the tech-

nology, policy of a government and global relation

among countries changes. Hence, application of trending

technique becomes more complicated if we will incor-

porate the above factors into account. However, it can be

suggested here that if a model can be prepared including

all of the above factors, accuracy of the forecasting can

be increased.

Here the daily peak load and minimum load are taken

into consideration separately. A week data has been ran-

domly selected then applying the proposed method the

next weeks data are evaluated then the optimum error

and absolute error were recorded and given in Tables 1

and 2. The result has been compared with the proposed

ANN model depicted in [14-15]. The average error for

peak load is mentio ned in the above article [1 6] is 2.35%

which is compared with the present result of this model

192 R. BERERA ET AL.

Table 1. Average minimum load forecasting for one week.

Time Actual load

(mw) Forecasted

load (mw) Error (mw) Percentage

Abs. error

3.00 32 32.7018 0.7018 2.17

27.00 30 29.8640 –0.14 0.468

51.00 33 33.6784 0.6784 2.02

75.00 23 24.7796 1.7796 7.18

99.00

Sunday 34 34.8234 0.8234 2.36

123.0 27 27.2701 0.2701 0.99

147.00 28 27.4538 0.54 1.96

Table 2. Average peak load forecasting for one week.

Time Actual load

(mw) Forecasted

load (mw) Error (mw) Percentage

Abs. error

18.00 64 62.8169 –1.1831 1.883

42.00 63 62.3988 –0.5012 0.803

69.00 55 56.0949 1.0949 1.951

94.00 58 56.2285 –1.7715 3.150

118.00

Sunday 52 53.5612 1.5612 2.914

141.0 0 54 55.6114 1.6114 2.897

165.00 54 53.8354 –0.1646 0.31

which is 1.986% and found suitable. Comparing to the

Sunday load forecasting with the holiday forecasting, the

former has 2.94% whereas the ANN model depicted in

[17-18] has 3.56% on average b a sis.

Here, a comparison between forecasted absolute error

for peak load and minimum load is given in Figure 2.

Also a comparison between actual peak load and fore-

casted peak load and that of minimum load are given in

Figure 3 and Figure 4 respectively. It can be observed

that the absolute error of peak load forecasting in case of

Saturday as shown in Figure 2 is much more than that of

the mini mum lo ad on the s ame day. The reason is due to

the government policy on declaration of holiday is dif-

ferent from that of the other parts of the world. Here

second Saturday of each month is given as a holiday.

However some of the organizations follow different

methods to declare Saturday as holiday.

4. Conclusions

The combined extrapolation and correlation technique

was tested and the results were presented as above. The

validation of the proposed model was compared with that

of the results mentioned in [19] of references mentioned

below and found su itable for predictio n.

Figure 2. Percentage abs. error for maximum and minimum

load diagram.

Figure 3. Peak forecasted load diagram.

Figure 4. Minimum forecasted load diagram.

R. BERERA ET AL.

193

5. References [10] H. Mori and M. Ohmi, “Probabilistic Short-Term Load

Forecasting with Gaussian Processes,” Proceedings of the

13th International Conference on Intelligent Systems

Application to Power Systems, Arlington, 6-10 November

2005, p. 6. doi:10.1109/ISAP.2005.1599306

[1] A. Goia, C. May and G. Fusai, “Functional Clustering

and Linear Regression for Peak Load Forecasting,” In-

ternational Journal of Forecasting, Vol. 26, No. 4, 2010,

pp. 700-711. doi:10.1016/j.ijforecast.2009.05.015 [11] H. Chen, C. A. Canizares and A. Singh, “ANN Based

Short-Term Load Forecasting in Electricity Markets,”

IEEE Proceedings of Power Engineering Society Winter

Meeting, Columbus, 28 January-1 February 2001, pp.

411-415. doi: 10.1109/PESW.2001.916876

[2] N. Amjady and A. Daraeepour, “Mixed Price and Load

Forecasting of Electricity Markets by a New Iterative

Prediction Method,” Electric Power Systems Research,

Vol. 79, No. 9, 2009, pp. 1329-1336.

doi:10.1016/j.epsr.2009.04.006 [12] H. S. Hippert, C. E. Pedreira and R. C. Souza, “Neural

Networks for Short-Term Load Forecasting: A Review

and Evaluation,” IEEE Transactions on Power Systems,

Vol. 16, No. 1, 2011, pp. 44-55. doi: 10.1109/59.910780

[3] A. D. Papalexopoulos and T. C. Hesterberg, “A Regres-

sion-Based Approach to Short-Term System Load Fore-

casting,” IEEE Transactions on Power Systems, Vol. 5,

No. 4, 1990, pp. 1535-1547. doi: 10.1109/59.99410 [13] W. Wang, C. Cheng and L. Qui, “Genetic Programming

with Rough Sets Theory for Modeling Short Term Load

Forecasting,” Fourth International Conference on Natural

Computational, Jinan, 18-20 October 2008, pp. 306-310.

doi: 10.1109/ICNC.2008.141

[4] S. Rahman and I. Moghram, “Application of Knowledge

Based Algorithms in Electric Utility Load Forecasting,”

IEEE Southeastern Conference on Energy and Informa-

tion Technologies, Richmond, March 1989, pp. 380-385.

doi:10.1109/SECON.1989.132399 [14] E. A. Feinberg, J. T. Hajagos and D. Genethliou, “Load

Pocket Modeling,” Proceedings of the 2nd IASTED In-

ternational Conference: Power and Energy Systems,

Crete, 25-28 June 2002, pp. 50-54.

[5] N. Kamel and Z. Baharudin, “Short-Term Load Forecast

Using Burg Autoregressive Technique,” MIT Press,

Cambridge, 1992. [15] D. Genethliou, “Statistical Load Modeling,” Proceedings

of the 7th IASTED International Multi-Conference: Power

and Energy Systems, Palm Springs, 2003, pp. 88-91.

[6] S. Fan, L. Chen and W. J. Lee, “Short-Term Load Fore-

casting Using Comprehensive Combination Based on

Multi-Meteorological Information,” IEEE Industrial and

Commercial Power Systems Technical Conference, Vol.

9, No. 2, 1994, pp. 1-7. [16] K.-B. Song, Y.-S. Baek, D. H. Hong and G. Jang,

“Short-Term Load Forecasting for Holidays Using Fuzzy

Linear Regression Method,” IEEE Transactions on

Power Systems, Vol. 20, No. 1, 2005, pp. 96-101.

doi: 10.1109/TPWRS.2004.835632

[7] C. S. Chen, J. C. Hwang, Y. M Tzeng, C. W. Huang and

M. Y. Cho, “Determination of Customer Load Character-

istics by Load Survey System,” IEEE Transactions on

Power Delivery, Vol. 11, No. 3, pp. 1430-1436.

doi: 10.1109/61.517501 [17] L. Vehviläinen and T. Pyykkönen, “Stochastic Factor

Model for Electricity Spot Price—The Case of the Nordic

Market,” Energy Economics, Vol. 27, No. 2, 2005, pp.

351-367. doi:10.1016/j.eneco.2005.01.002

[8] T. W. S Chow and C. T. Leung, “Nonlinear Autoregres-

sive Integrated Neural Network Model for Short-Term

Load Forecasting,” IEE Proceedings of Generation,

Transmission and Distribution, Vol. 143, No. 5, 1996, pp.

500-506. doi: 10.1049/ip-gtd:19960600

[18] A. G. Baklrtzis, V. Petrldis, S. J. Klartzls, M. C. Alex-

ladls and A. H. Malssls, “A Neural Network Short Term

Load Forecasting Model for The Greek Power System”

IEEE Transaction on Power Systems, Vol. 11, No. 2,

1996, pp. 858-863. doi: 10.1109/59.496166

[9] S. Rahman and O. Hazim, “Load Forecasting for Multiple

Sites: Development of an Expert System-Based Tech-

nique,” Electric Power Systems Research, Vol. 39, No. 3,

1996, pp. 161-169. doi:10.1016/S0378-7796(96)01114-5 [19] R. L. Sullivan, “Power System Planning,” McGraw-Hill

International Book Company, New York, 1997.