Abstract
Electric Power Load Forecasting is important for the economic and secure operation of power system, and highly accurate forecasting result leads to substantial savings in operating cost and increased reliability of power supply. Conventional load forecasting techniques, including time series methods and stochastic methods, are widely used by electric power companies for forecasting load profiles. However, their accuracy is limited under some conditions. In this paper, neural networks have been successfully applied to load forecasting. Forecasting model with Neural Networks is set up based on the analysis of the characteristics of electric power load, and it works well even with rapidly changing weather conditions. This paper also proposes a novel method to improve the generalization ability of the Neural Networks, and this leads to further increasing accuracy of load forecasting.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Song, K., Baek, Y., Hong, D., Jang, G.: Short-Term Load Forecasting for the Holidays Using Fuzzy Linear Regression Method. IEEE Transactions on Power Systems 20(1), 96–101 (2005)
Charytoniuk, W., Chen, M.S., Van Olinda, P.: Nonparametric Regression Based Short-Term Load Forecasting. IEEE Transactions on Power Systems 13(3), 725–730 (1998)
Fan, J.Y., McDonald, J.D.: A Real-Time Implementation of Short-Term Load Forecasting for Distribution Power Systems. IEEE Transactions on Power Systems 9(2), 988–994 (1994)
Rahman, S., Hazim, O.: Load Forecasting for Multiple Sites: Development of an Expert System-Based Technique. Electric Power Systems Research 39(3), 161–169 (1996)
Papadakis, S.E., Theocharis, J.B., Bakirtzis, A.G.: A load curve based fuzzy modeling technique for short-term load forecasting. Fuzzy Sets and Systems 135(2), 279–303 (2003)
Ruzic, S., Vuckovic, A., Nikolic, N.: Weather Sensitive Method for Short-Term Load Forecasting in Electric Power Utility of Serbia. IEEE Transactions on Power Systems 18(4), 1581–1586 (2003)
Chen, H., Canizares, C.A., Singh, A.: ANN-Based Short-Term Load Forecasting in Electricity Markets. In: Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference, vol. 2, pp. 411–415 (2001)
Khotanzad, A., Rohani, R.A., Maratukulam, D.: ANNSTLF– Artificial Neural Network Short-Term Load Forecaster–Generation Three. IEEE Transactions on Power Systems 13(4), 1413–1422 (1998)
Taylor, J.W., Buizza, R.: Using Weather Ensemble Predictions in Electricity Demand Forecasting. International Journal of Forecasting 19(1), 57–70 (2003)
Feng, N., Wang, F., Qiu, Y.: Novel Approach for Promoting the Generalization Ability of Neural Networks. International Journal of Signal Processing 2(2), 1304–4478 (2005)
Inoue, H., Narihisa, H.: Improving Generalization Ability of Self-Generating Neural Networks Through Ensemble Averaging. In: Terano, T., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 177–180. Springer, Heidelberg (2000)
Franco, L., Cannas, S.A.: Generalization and Selection of Examples in Feed forward Neural Networks. Neural Computation 12(10), 2405–2426 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, H., Li, BS., Han, XY., Wang, DL., Jin, H. (2006). Study of Neural Networks for Electric Power Load Forecasting. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_185
Download citation
DOI: https://doi.org/10.1007/11760023_185
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
eBook Packages: Computer ScienceComputer Science (R0)