Improved bacterial foraging optimization algorithm with machine learning driven short term electricity load forecasting: A case study in Peninsular Malaysia

Accurate electricity demand forecasting is crucial for ensuring the sustainability and reliability of power systems. Least square support vector machines (LSSVM) are well suited to handle complex non‑linear power load series. However, the less optimal regularization parameter and the Gaussian kerne...

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Bibliographic Details

Main Authors:	Sulaima, Mohamad Fani, Zaini, Farah Anishah, Wan Abdul Razak, Intan Azmira, Othman, Mohammad Lutfi, Mokhlis, Hazlie
Format:	Article
Language:	en
Published:	Multidisciplinary Digital Publishing Institute (MDPI) 2024
Online Access:	http://eprints.utem.edu.my/id/eprint/28598/2/01193301220241157111545.pdf http://eprints.utem.edu.my/id/eprint/28598/ https://www.mdpi.com/1999-4893/17/11/510 https://doi.org/10.3390/ a17110510
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