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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 249208, 31 pages
http://dx.doi.org/10.1155/2014/249208
Research Article

A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price

1Department of Basic Courses, Lanzhou Institute of Technology, Lanzhou 730050, China
2School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China

Received 2 April 2014; Accepted 19 May 2014; Published 14 July 2014

Academic Editor: Fuding Xie

Copyright © 2014 Zhilong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO), the backpropagation artificial neural network (BPANN), and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN) method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.