Improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular Malaysia

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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 kernel...

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Bibliographic Details
Main Authors:	Zaini, Farah Anishah, Sulaima, Mohamad Fani, Wan Abdul Razak, Intan Azmira, Othman, Mohammad Lutfi, Mokhlis, Hazlie
Format:	Article
Language:	English
Published:	Multidisciplinary Digital Publishing Institute 2024
Online Access:	http://psasir.upm.edu.my/id/eprint/114311/ http://psasir.upm.edu.my/id/eprint/114311/1/114311.pdf

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