期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 27, 期 4, 页码 2055-2062出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2012.2190627
关键词
Bootstrapping; extreme learning machine; interval forecast; price forecast
资金
- Hong Kong RGC GRF Grant [515110]
- State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University) [2007DA10512711401]
Artificial neural networks (ANNs) have been widely applied in electricity price forecasts due to their nonlinear modeling capabilities. However, it is well known that in general, traditional training methods for ANNs such as back-propagation (BP) approach are normally slow and it could be trapped into local optima. In this paper, a fast electricity market price forecast method is proposed based on a recently emerged learning method for single hidden layer feed-forward neural networks, the extreme learning machine (ELM), to overcome these drawbacks. The new approach also has improved price intervals forecast accuracy by incorporating bootstrapping method for uncertainty estimations. Case studies based on chaos time series and Australian National Electricity Market price series show that the proposed method can effectively capture the nonlinearity from the highly volatile price data series with much less computation time compared with other methods. The results show the great potential of this proposed approach for online accurate price forecasting for the spot market prices analysis.
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