Optimization model of electricity metering management based on MOPSO

In response to the difficulty of balancing economy and accuracy in traditional energy metering management methods, an improved particle swarm optimization model is designed to optimize energy metering management based on multi-objective particle swarm optimization, thereby achieving optimal resource...

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Bibliographic Details
Published in:Renewables : wind, water, and solar Vol. 12; no. 1; pp. 29 - 19
Main Authors: Li, Sheng, Zhou, Xiaodan
Format: Journal Article
Language:English
Published: London BioMed Central 05.06.2025
Springer Nature B.V
SpringerOpen
Subjects:
Accuracy
Algorithms
Computer applications
Economics and Management
Electric power loss
Electricity metering management
Energy
Energy Policy
Energy Systems
Management
Management methods
Multi-objective particle swarm algorithm
Multiple objective analysis
Operating costs
Optimization
Optimization models
Particle swarm optimization
PSO
Renewable and Green Energy
Resource allocation
Test
Water Industry/Water Technologies
Multi-objective particle swarm algorithm
Test
PSO
Optimization
Electricity metering management
ISSN:2731-9237, 2731-9237, 2198-994X
Online Access:Get full text
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Summary:In response to the difficulty of balancing economy and accuracy in traditional energy metering management methods, an improved particle swarm optimization model is designed to optimize energy metering management based on multi-objective particle swarm optimization, thereby achieving optimal resource allocation and maximizing management efficiency. The study uses Schaffer and Griewank functions to test its performance. The optimized algorithm performed well on both Schaffer and Griewank functions, indicating that the algorithm still had high computational efficiency while ensuring computational accuracy. In the case analysis, the MOPSO algorithm achieved mean absolute error, absolute percentage error, root mean square error, and mean absolute percentage error values of 8.61%, 3.08%, 0.2158, and 0.1295, respectively. Moreover, the line loss prediction curve closely matched the actual values. In addition, before and after the model was connected, the total active power loss decreased by 43.55 kW, and the operating costs decreased by a total of 2.2111 million RMB. The optimization model based on MOPSO algorithm can achieve both accuracy and economy in energy metering management.
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ISSN:2731-9237
2731-9237
2198-994X
DOI:10.1186/s40807-025-00175-x