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Energies 2012, 5(11), 4711-4725; doi:10.3390/en5114711

Day-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Analysis Network

Department of Electrical Engineering, Chung Yuan Christian University, 200 Chung Pei Road, Chung Li 32023, Taiwan
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Received: 5 September 2012 / Accepted: 12 November 2012 / Published: 19 November 2012
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
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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 that the proposed method is capable of forecasting day-ahead LMP values efficiently.
Keywords: locational marginal price; forecasting; principal component analysis locational marginal price; forecasting; principal component analysis
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Hong, Y.-Y.; Wu, C.-P. Day-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Analysis Network. Energies 2012, 5, 4711-4725.

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