Short-Term Electricity Price Forecasting Based on MPSO-BP Algorithm

Abstract:

In this paper, we exploited the short-term electricity price forecasting issue by introducing a global search mechanism based on the improved particle swarm optimization (MPSO) algorithm for the neural network training. The proposed MPSO algorithm is used for the initial weights and threshold of BP neural network in the process of optimization. We then proposed a novel short-term electricity price forecasting model based on MPSO-BP neural network. The paper provides a number of examples of bidding model of the California electricity market to forecasting market clear price using BP neural network trained by MPSO. Through the comparative study of the conventional BP neural network and the proposed MPSO-BP neural network, the proposed method demonstrates improved performance in finding the optimal solution with excellent convergence time for all the simulated scenarios.

• Periodical: Advanced Materials Research (Volumes 591-593)
• Pages: 1351-1355
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.591-593.1351
• Online since: November 2012
• Authors: Yu Dong, Qiang Yang, Wen Jun Yan
• Keywords: BP Neural Network (BPNN), Electricity Price Forecasting, MPSO-BP, Particle Swarm Optimization (PSO)

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