A Hybrid Short Term Load Forecasting Model of an Indian Grid
R. Behera, B. P. Panigrahi, B. B. Pati
DOI: 10.4236/epe.2011.32024   PDF    HTML     9,568 Downloads   15,718 Views   Citations


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.

Share and Cite:

R. Behera, B. Panigrahi and B. Pati, "A Hybrid Short Term Load Forecasting Model of an Indian Grid," Energy and Power Engineering, Vol. 3 No. 2, 2011, pp. 190-193. doi: 10.4236/epe.2011.32024.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] A. Goia, C. May and G. Fusai, “Functional Clustering and Linear Regression for Peak Load Forecasting,” International Journal of Forecasting, Vol. 26, No. 4, 2010, pp. 700-711. doi:10.1016/j.ijforecast.2009.05.015
[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
[3] A. D. Papalexopoulos and T. C. Hesterberg, “A Regression-Based Approach to Short-Term System Load Forecasting,” IEEE transactions on Power Systems, Vol. 5, No. 4, 1990, pp. 1535-1547. doi: 10.1109/59.99410
[4] S. Rahman and I. Moghram, “Application of Knowledge Based Algorithms in Electric Utility Load Forecasting,” IEEE Southeastern Conference on Energy and Information Technologies, Richmond, March 1989, pp. 380-385. doi:10.1109/SECON.1989.132399
[5] N. Kamel and Z. Baharudin, “Short Term Load Forecast Using Burg Autoregressive Technique,” MIT Press, Cambridge, 1992.
[6] S. Fan, L. Chen and W. J. Lee, “Short-Term Load Forecasting Using Comprehensive Combination Based on Multi-Meteorological Information,” IEEE Industrial and Commercial Power Systems Technical Conference, Vol. 9, No. 2, 1994, pp. 1-7.
[7] C. S. Chen, J. C. Hwang, Y. M Tzeng, C. W. Huang and M. Y. Cho, “Determination of Customer Load Characteristics by Load Survey System,” IEEE Transactions on Power Delivery, Vol. 11, No. 3, pp. 1430-1436. doi: 10.1109/61.517501
[8] T. W. S Chow and C. T. Leung, “Nonlinear Autoregressive 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
[9] S. Rahman and O. Hazim, “Load Forecasting for Multiple Sites: Development of an Expert System-Based Technique,” Electric Power Systems Research, Vol. 39, No. 3, 1996, pp. 161-169. doi:10.1016/S0378-7796(96)01114-5
[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
[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, January 28-February 1, 2001, pp. 411-415. doi: 10.1109/PESW.2001.916876
[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
[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
[14] E. A. Feinberg, J. T. Hajagos and D. Genethliou, “Load Pocket Modeling,” Proceedings of the 2nd IASTED International Conference: Power and Energy Systems, Crete, 25-28 June 2002, pp. 50-54.
[15] D. Genethliou, “Statistical Load Modeling,” Proceedings of the 7th IASTED International Multi-Conference: Power and Energy Systems, Palm Springs, 2003, pp. 88-91.
[16] K.-Bin Song, Y.-Sik 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
[17] L. Vehvil?inen and T. Pyykk?nen, “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
[18] A. G. Baklrtzis, V. Petrldis, S. J. Klartzls, M. C. Alexladls 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
[19] R. L. Sullivan, “Power System Planning,” McGraw-Hill International Book Company, 1997.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.