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.
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© 2006 Springer-Verlag Berlin Heidelberg
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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
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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
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