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Research on Technological Efficiency of Electric Power Using Three-stage DEA and Machine Learning Methods

Published: 31 July 2024 Publication History

Abstract

With a large proportion of electric power equipment in China approaching their end of service life, the electric power system is striving to enhance the technological efficiency of equipment renewal through engineering methodologies. This paper aims to tackle the lurking threat of system reliability by constructing a systemic framework to evaluate and estimate the efficiency of electric power companies. The approach can guide the optimal planning strategy for updating equipment across the country, depending on the efficiency evaluation. Firstly, using three-stage data envelopment analysis (DEA) method, this paper assesses the comprehensive efficiencies of 12 typical electronic power companies in China from 2019 to 2023 under circumstances of equipment renewal, regarding efficiencies as the measurement of technological benefit to the whole system. Secondly, to address future events and overcome DEA's disadvantage of inconsistent benchmark, multilayer perceptron (MLP), a machine learning framework is applied to estimate the efficiency level based on the variables shared with the three-stage DEA method. Finally, this paper assesses the accuracy of DEA-MLP estimation and confirmed the robustness of the framework. This paper's results can help to evaluate the equipment renewal's technological performance from a holistic view of inputs and outputs and deal with future crises in harsh situations of electric power system.

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  1. Research on Technological Efficiency of Electric Power Using Three-stage DEA and Machine Learning Methods

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

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    Published: 31 July 2024

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