Data-Driven Risk Warning of Electricity Sales Companies in the Whole Business Process

Biyun Chen, Tianwang Fu (), Liming Wei, Rong Zheng, Zhe Lin, Haiwei Liu and Zhijun Qin
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Biyun Chen: Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China
Tianwang Fu: Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China
Liming Wei: Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China
Rong Zheng: China Southern Power Grid Guangxi Electric Power Trading Center, Nanning 530023, China
Zhe Lin: China Southern Power Grid Guangxi Electric Power Trading Center, Nanning 530023, China
Haiwei Liu: Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China
Zhijun Qin: Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China

Sustainability, 2025, vol. 17, issue 9, 1-22

Abstract: As China’s power market reforms deepen, the scale of market operations and the number of participants have reached new highs, introducing increasingly complex threats and heightened risk scenarios. Traditional risk early warning systems for electricity sales companies are heavily influenced by subjective factors, incomplete data, and poor real-time performance, which cannot meet the requirements of sustainable development. To achieve efficient, full-chain, and sustainable risk control, this paper proposes a data-driven risk warning method for electricity sales companies, encompassing the entire sales process. Firstly, based on data correlations across the electricity sales process, appropriate data sources for risk warnings are identified. Key elements are then extracted using Principal Component Analysis (PCA), while historical business data is adaptively clustered, with risk warning levels classified using the Adaptive Sparrow Optimization Density Peak Clustering Algorithm (DPC-SSA). Lastly, dynamic risk warnings are generated through the stacking identification model. The effectiveness and practicality of the proposed method are validated through an analysis using real data from a provincial power trading management platform.

Keywords: electricity sales company; sustainable risk warning; data-driven; stacking model; whole process (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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