NDLI: Short-term load forecasting with artificial neural network and fuzzy logic

Content Provider	IEEE Xplore Digital Library
Author	Ma-WenXiao Bai-XiaoMin Mu-LianShun
Copyright Year	2002
Description	Author affiliation: China Electr. Power Res. Inst., Beijing, China (Ma-WenXiao; Bai-XiaoMin; Mu-LianShun)
Abstract	Short-term load forecasting, which forecasts the electric load of the future day or week, is basis not only for power generation and operation, but also for power market contract. Short-term load forecasting has become an essential part of modern control centers. In the past three decades, many forecasting models have been presented. This paper proposes a new model, which divides the electric load into two parts: the load scaled curve and the day maximal load and minimal load. The load scaled curve is forecasted using five artificial neuron networks. The day maximal load and minimal load are forecasted using fuzzy logic. In this model, weather condition, seasonal index, Test shows the proposed method in this paper can improve the forecasting accuracy.
Sponsorship	IEEE Power Eng. Soc. (IEEE PES) Chinese Soc. Electr. Eng. (CSEE) China Electric Power Res. Inst. IEEE/PES Beijing Chapter
Starting Page	1101
Ending Page	1104
File Size	343834
Page Count	4
File Format	PDF
ISBN	0780374592
DOI	10.1109/ICPST.2002.1047571
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-10-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load forecasting Artificial neural networks Fuzzy logic Weather forecasting Economic forecasting Predictive models Power generation Power markets Contracts Neurons
Content Type	Text
Resource Type	Article

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