期刊
ELECTRIC POWER SYSTEMS RESEARCH
卷 78, 期 8, 页码 1332-1342出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.epsr.2007.12.001
关键词
electricity price forecasting; artificial neural networks; Levenberg-Marquardt algorithm; sensitivity analysis; fuzzy clustering method
Over the past 15 years most electricity supply companies. around the world have. been restructured from monopoly utilities to deregulated competitive electricity markets. Market participants in the restructured electricity markets find short-term electricity price forecasting (STPF) crucial in formulating their risk management strategies. They need to know future electricity prices as their profitability depends on them. This research project classifies and compares different techniques of electricity price forecasting in the literature and selects artificial neural networks (ANN) as a suitable method for price forecasting. To perform this task, market knowledge should be used to optimize the selection of input data for an electricity price forecasting tool. Then sensitivity analysis is used in this research to aid, in the selection of the optimum inputs of the ANN and fuzzy c-mean (FCM) algorithm is used for daily load pattern clustering. Finally, ANN with a modified Levenberg-Marquardt (LM) learning algorithm are implemented for forecasting prices in Pennsylvania-New Jersey-Maryland (PJM) market. The forecasting results were compared with the previous works and showed that the results are reasonable and accurate. (C) 2007 Elsevier B.V. All rights reserved.
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