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Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping

2012; Institute of Electrical and Electronics Engineers; Volume: 27; Issue: 4 Linguagem: Inglês

10.1109/tpwrs.2012.2190627

1558-0679

Xia Chen, Zhao Yang Dong, Ke Meng, Yan Xu, Kit Po Wong, H.W. Ngan,

Power Systems and Renewable Energy

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.