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RESEARCH ARTICLE

Optimized demand-side management for large power consumers using PSO and MLIP algorithms: A case study of the Western Cape municipality

Abuyile Mpaka1* ,  Senthil Krishnamurthy1
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1 Department of Electrical, Electronics and Computer Engineering, Center for Intilligent Systems and Emerging Technology, Faculty of Engineering and Built Environment, Cape Peninsula University of Technology, Cape Town, Western Cape Province, South Africa
IJOCTA, 025310136 https://doi.org/10.36922/IJOCTA025310136
Received: 31 July 2025 | Revised: 22 September 2025 | Accepted: 24 September 2025 | Published online: 5 November 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

The increasing power demand, transmission line congestion, and increasing electricity traffic necessitate the effective implementation of demand-side management (DSM) strategies to improve energy efficiency and sustainability. This research presents an optimized DSM framework for large power consumers in the Western Cape municipality, utilizing particle swarm optimization (PSO) integrated with machine learning improved prediction algorithms to achieve peak clipping and reduce peak load power demand under real-time pricing conditions. The developed algorithms were validated using actual energy consumption data from large industrial customers in the Western Cape province. Simulation results indicate that the PSO-driven DSM framework significantly reduces peak demand, improves the load factor, and offers substantial cost savings compared to conventional load management techniques. This study highlights the potential of intelligent optimization methods to support municipalities and major energy users in adopting more flexible, affordable, and sustainable energy consumption practices.

Graphical abstract
Keywords
Demand side management
Energy efficiency
Large power user
Load management
Machine learning improved prediction technique
Particle swarm optimization
Smart grids
Funding
This work was supported in part by the National Research Foundation of South Africa under Thuthuka Grant No. 138177, in part by the Eskom Tertiary Education Support Program through a research grant and in part by the Eskom Power Plant Engineering Institute.
Conflict of interest
The authors declare they have no competing interests.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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