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day-ahead electricity price forecasting using a hybrid principal component analysis network日前电价预测使用主成分分析的混合网络
Energies 2012, 5, 4711-4725; doi:10.3390/en5114711 OPEN ACCESS energies ISSN 1996-1073 /journal/energies Article Day-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Analysis Network Ying-Yi Hong * and Ching-Ping Wu Department of Electrical Engineering, Chung Yuan Christian University, 200 Chung Pei Road, Chung Li 32023, Taiwan; E-Mail: squall59@.tw * Author to whom correspondence should be addressed; E-Mail: yyhong@.tw; Tel.: +886-3-265-1200; Fax: +886-3-265-4809. Received: 5 September 2012; in revised form: 10 November2012 / Accepted: 12 November 2012 / Published: 19 November 2012 Abstract: Bidding competition is one of the main transaction approaches in a deregulated electricity market. Locational marginal prices (LMPs) resulting from bidding competition and system operation conditions indicate electricity values at a node or in an area. The LMP reveals important information for market participants in developing their bidding strategies. Moreover, LMP is also a vital indicator for the Security Coordinator to perform market redispatch for congestion management. This paper presents a method using a principal component analysis (PCA) network cascaded with a multi-layer feedforward (MLF) network for forecasting LMPs in a day-ahead market. The PCA network extracts essential features from periodic information in the market. These features serve as inputs to the MLF network for forecasting LMPs. The historical LMPs in the PJM market are employed to test the proposed method. It is found tha
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