Research on Power Marketing Decision-Making Algorithm Based on Bayesian Network

Ying Guo (), Yang Ni, Yuan Wang, Jing Zhao and Liwei Liu
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Ying Guo: Marketing Service Center (Metering Center) of State Grid Hubei Electric Power Co., Ltd
Yang Ni: Marketing Service Center (Metering Center) of State Grid Hubei Electric Power Co., Ltd
Yuan Wang: Marketing Service Center (Metering Center) of State Grid Hubei Electric Power Co., Ltd
Jing Zhao: Marketing Service Center (Metering Center) of State Grid Hubei Electric Power Co., Ltd
Liwei Liu: Marketing Service Center (Metering Center) of State Grid Hubei Electric Power Co., Ltd

A chapter in Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023), 2024, pp 1245-1253 from Springer

Abstract: Abstract With the rapid development of the economy, fully leveraging the priority role of electricity is of great significance for accelerating the modernization of electricity, continuously meeting the growing demand for electricity and social production and consumption, and promoting socio-economic development. Among them, the role of electricity marketing is becoming increasingly prominent. How to fully mine and utilize the large amount of power marketing data accumulated by electric power enterprises over the years, so as to provide reliable support for the analysis and research of power marketing decisions. Bayesian network is a graphical pattern used to represent the continuous probability distribution of a set of variables, which provides causal information to discover potential relationships between data, and because of these characteristics, it is widely used in data mining. Therefore, this paper applies Bayesian network to the analysis and research of power marketing decision, and establishes a Bayesian network suitable for power marketing decision-making for customer value evaluation and provides reliable support for marketing decision-making.

Keywords: Bayesian network; Power marketing decisions; Data mining (search for similar items in EconPapers)
Date: 2024
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DOI: 10.2991/978-94-6463-256-9_127

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