A NOVEL HYBRID APPROACH TO SHORT TERM LOAD FORECASTING

Year 2011, Volume: 11 Issue: 1, 1345 - 1354, 28.03.2012

Ashish Kumar Srıvastava Tariqul Islam

Abstract

The knowledge of a day ahead load is necessary for a utility in a competitive electricity market for fuel purchase scheduling, planning for energy transactions and to maintain their power reserve close to the minimum as required by Independent System Operator. Previous researches do not consider the effect of wind direction on load forecasting, however this paper investigates the effect of wind direction and weather event on load requirements and accordingly presents a novel Neuro-Fuzzy based approach to Short term load forecast (STLF) i.e. a day ahead average load forecast utilizing parameters identified as historical load, temperature, weather event (for e.g. fog and snow) and wind direction. Four different input structures, three using Neuro-Fuzzy approach and one using only Neural network (NN) are tested. Among the four input structures, structure utilizing Neuro-Fuzzy approach with wind direction as one of the input parameters gives impressive result, with an average error of 1.735 %. The model is trained and tested on load and weather data pertaining to Norwalk/Stamford in Connecticut Valley Electric Exchange.

Keywords: Artificial Neural Network (ANN), Neural network (NN), Short term load forecasting (STLF), Multi Layer Perceptron (MLP), Simulation.

Keywords

Artificial Neural Network (ANN), Neural network (NN), Short term load forecasting (STLF), Multi Layer

References

• D. C. Park, M. A. El-Sharkawi, R. J. Marks II, L. E. Atlas and M. J. Damborg, “Electric Load Forecasting Using An Artificial Neural Network”, IEEE Transactions on Power Systems, vol. 6, no. 2, pp. 442–449, May 1991.
• G. E. P. Box and G. M. Jenkins, “Time Series Analysis— Forecasting and Control”, San Francisco, CA: HoldenDay, 1976.
• A. D. Papalexopoulos and T. C. Hesterberg, “A regression-based approach to short-term load forecasting”, IEEE Trans. Power Syst., vol. 5, no. 4, pp. 1535–1550, Nov. 1990.
• S. Rahman and O. Hazim, “A Generalized KnowledgeBased Short Term Load Forecasting Technique”, IEEE Trans. Power Syst., vol. 8, no. 2, pp. 508–514, May 1993.
• T. Haida and S. Muto, “Regression based peak load forecasting using a transformation technique”, IEEE Trans. Power Syst., vol. 9, no. 4, pp. 1788–1794, Nov. 1994.
• D. G. Infield and D. C. Hill, “Optimal smoothing for trend removal in short term electricity demand forecasting”, IEEE Trans. Power Syst., vol.13, no. 3, pp. 1115–1120, Aug. 1998.
• S. J. Huang and K. R. Shih, “Short-term load forecasting via ARMA model identification including non-Gaussian process considerations”, IEEE Trans. Power Syst., vol. 18, no. 2, pp. 673–679, May 2003.
• H.Wu and C. Lu, “A data mining approach for spatial modeling in small area load forecast”, IEEE Trans. Power Syst., vol. 17, no. 2, pp. 516–521, May 2003.
• T. S. Dillon, S.Sestito and S. Leung, “Short term load forecasting using an adaptive neural network”, International Journal of Electrical Power and Energy Systems, 13(4), 186-192, August 1991.
• T. M. Peng, N. F. Hubele, G. G. Karady, “ADVANCEMENT IN THE APPLICATION OF NEURAL NETWORK FOR SHORT-TERM LOAD FORECASTING”, IEEE Transactions on Power Systems, vol. 7, no. 1, pp. 250–257, Feb. 1992.
• K. Y .Lee, Y. T. Cha, J. H. Park, “SHORT-TERM LOAD FORECASTING USING AN ARTIFICIAL NEURAL NETWORK”, IEEE Transactions on Power Systems, vol. 7, no. 1, pp. 124–132, Feb. 1992.
• Alireza Khotanzad, Rey-Chue Hwang, Alireza Abaye and Dominic Maratukulam, “An Adaptive Modular Artificial Neural Network Hourly Load Forecaster and its Implementation at Electric Utilities”, IEEE Transactions on Power Systems, vol. 10. no. 3, pp.1716-1722, August 1995.
• Dipti Srinivasan, C.S.Chang and A.C. Liew, “Demand Forecasting Using Fuzzy Neural Computation, With Special Emphasis On Weekend And Public Holiday Forecasting”, IEEE Transactions on Power Systems, vol. 10, no. 4, pp. 1897–1903, Nov. 1995.
• P. K. Dash, A. C.Liew and S. Rahman,“Peak load forecasting using a fuzzy neural network”,Electrical Power System Research, 32(1), 19-23,1995.
• J. Vermaak and E .C. Botha, “Recurrent Neural Networks for Short-Term Load Forecasting”, IEEE Transactions on Power Systems, vol. 13, no. 1, pp.126132, February 1998.
• R. H. Liang and C. C. Cheng,“Short-term load forecasting by a neuro-fuzzy based approach”, International Journal of Electrical Power and Energy Systems, 24(2),103-111, 2002.
• B. K. Chauhan, A. Sharma and M. Hanmandlu, “NEURO FUZZY APPROACH BASED SHORT TERM ELECTRIC LOAD FORECASTING”, IEEE/PES Transmission and Distribution Conference and Exhibition: Asia and Pacific, Dalian, China, 2005.
• Siqing Sheng and Cong Wang, “Integrating Radial Basis Function Neural Network with Fuzzy Control for Load Forecasting in Power System”, IEEE/PES Asia and Pacific, Dalian, China, pp.1-5, 2005.
• Shu Fan and Luonan Chen, “Short-Term Load Forecasting Based on an Adaptive Hybrid Method”, IEEE Transactions on Power Systems, vol.21, no.1, pp.392-401, February 2006.
• A. K.Topalli, I. Erkmen, and I. Topalli, “Intelligent shortterm load forecasting in Turkey”, International Journal of Electrical Power and Energy Systems,28(7), 437-447, 2006.
• Liam Paull, Howard Li and Liuchen Chang, “THE DEVELOPMENT OF A FUZZY NEURAL SYSTEM FOR LOAD FORECASTING,” CCECE/CCGEI, Niagara Falls, Canada, pp. 923-926, May 5-7, 2008.
• Shu Fan, Luonan Chen and Wei-Jen Lee, “Short-Term Load Forecasting Using Comprehensive Combination Based on Multimeteorological Information,” IEEE Transactions on Industry Applications, vol. 45, no. 4, pp.1460-1466, Jul./Aug. 2009.
• John Yen and Liang Wang, “Simplifying Fuzzy Rule-Based Models Using Orthogonal Transformation Methods,” IEEE Transactions on Systems, Man, and Cybernetics-Part B, vol. 29, no. 1, pp.13-24, Feb. 1999.
• S. Haykin, “Neural Networks: A Comprehensive Foundation,” Second Edition, Pearson Education; 2006.
• John Yen and Reza Langari, “ Fuzzy Logic-Intelligence, Control and Information,” Pearson Education, Low Price Edition, Fourth Indian Reprint; 2005.
• Oxford School Atlas, Third Edition, Oxford University Press, New Delhi.
• Amos Gilat, “Matlab: An Introduction with Applications,” Wiley-India Edition.
• Fuzzy logic and NN Tool Box Manual-Users Guide: Mathworks Version 7.3.0.267.
• www.cvx.com
• www.wunderground.com