Application of Comprehensive Evaluation of Line Loss Lean Management Based on Big-Data-Driven Paradigm

Bin Li, Yuxiang Tan (), Qingqing Guo and Weihuan Wang
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Bin Li: Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China
Yuxiang Tan: Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China
Qingqing Guo: Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China
Weihuan Wang: Guangxi Key Laboratory of Power System Optimization and Energy-Saving Technology, School of Electrical Engineering, Guangxi University, Nanning 530004, China

Sustainability, 2023, vol. 15, issue 15, 1-21

Abstract: Effective line loss management necessitates a model-driven evaluation method to assess its efficiency level thoroughly. This paper introduces a “model-driven + data-driven” approach based on collective intelligence theory to address the limitations of individual evaluation methods in conventional line loss assessments. Initially, eight different evaluation methods are used to form collective intelligence to evaluate the line loss management of power grid enterprises and generate a comprehensive dataset. Then, the data set is trained and evaluated using the random forest algorithm, with Spearman rank correlation coefficient as the test metric, to assess the power grid enterprise’s line loss management level. Combining model-driven and data-driven methods, this integrated approach efficiently leverages the informational value of indicator data while thoroughly considering the causal and associative attributes within the dataset. Based on data from 61 municipal grid enterprises, both the comparison of multiple AI methods and correlation tests of results verify the superiority of the proposed method.

Keywords: line loss management; evaluation system; random forest algorithm; big-data-driven; artificial intelligence; collective intelligence (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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