Journal
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
Volume 30, Issue 9, Pages 533-546Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ijepes.2008.06.001
Keywords
Price forecast; Mixed data model; Hybrid forecast method
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In a competitive electricity market, forecast of energy prices is a key information for the market participants. However, price signal usually has a complex behavior due to its nonlinearity, nonstationarity, and time variancy. In spite of all performed researches on this area in the recent years, there is still an essential need for more accurate and robust price forecast methods. In this paper, a combination of wavelet transform (WT) and a hybrid forecast method is proposed for this purpose. The hybrid method is composed of cascaded forecasters where each forecaster consists of a neural network (NN) and an evolutionary algorithms (EA). Both time domain and wavelet domain features are considered in a mixed data model for price forecast, in which the candidate input variables are refined by a feature selection technique. The adjustable parameters of the whole method are fine-tuned by a cross-validation technique. The proposed method is examined on PJM electricity market and compared with some of the most recent price forecast methods. (C) 2008 Elsevier Ltd. All rights reserved.
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