Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game

• Main Authors: Yajing Gao, Xiaojie Zhou, Jiafeng Ren, Zheng Zhao, Fushen Xue
• Format: Article
• Language: English
• Published: MDPI AG 2018-04-01
• Series: Energies
• Subjects: power retailer; load forecasting; fuzzy clustering; price forecasting; Bayesian game
• Online Access: http://www.mdpi.com/1996-1073/11/5/1063

The openness of the electricity retail market results in the power retailers facing fierce competition in the market. This article aims to analyze the electricity purchase optimization decision-making of each power retailer with the background of the big data era. First, in order to guide the power retailer to make a purchase of electricity, this paper considers the users’ historical electricity consumption data and a comprehensive consideration of multiple factors, then uses the wavelet neural network (WNN) model based on “meteorological similarity day (MSD)” to forecast the user load demand. Second, in order to guide the quotation of the power retailer, this paper considers the multiple factors affecting the electricity price to cluster the sample set, and establishes a Genetic algorithm- back propagation (GA-BP) neural network model based on fuzzy clustering (FC) to predict the short-term market clearing price (MCP). Thirdly, based on Sealed-bid Auction (SA) in game theory, a Bayesian Game Model (BGM) of the power retailer’s bidding strategy is constructed, and the optimal bidding strategy is obtained by obtaining the Bayesian Nash Equilibrium (BNE) under different probability distributions. Finally, a practical example is proposed to prove that the model and method can provide an effective reference for the decision-making optimization of the sales company.