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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Multidisciplinary Digital Publishing Institute
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/114311/ http://psasir.upm.edu.my/id/eprint/114311/1/114311.pdf |
| _version_ | 1848866455835115520 |
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| author | Zaini, Farah Anishah Sulaima, Mohamad Fani Wan Abdul Razak, Intan Azmira Othman, Mohammad Lutfi Mokhlis, Hazlie |
| author_facet | Zaini, Farah Anishah Sulaima, Mohamad Fani Wan Abdul Razak, Intan Azmira Othman, Mohammad Lutfi Mokhlis, Hazlie |
| author_sort | Zaini, Farah Anishah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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 function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a new hybrid model based on the LSSVM optimized by the improved bacterial foraging optimization algorithm (IBFOA) for forecasting the short-term daily electricity load in Peninsular Malaysia. The IBFOA based on the sine cosine equation addresses the limitations of fixed chemotaxis constants in the original bacterial foraging optimization algorithm (BFOA), enhancing its exploration and exploitation capabilities. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using mean absolute percentage error (MAPE) as the objective function. The comparative analysis demonstrates the model, achieving the highest determination coefficient (R2) of 0.9880 and significantly reducing the average MAPE value by 28.36%, 27.72%, and 5.47% compared to the deep neural network (DNN), LSSVM, and LSSVM-BFOA, respectively. Additionally, IBFOA exhibits faster convergence times compared to BFOA, highlighting the practicality of LSSVM-IBFOA for short-term load forecasting. |
| first_indexed | 2025-11-15T14:20:53Z |
| format | Article |
| id | upm-114311 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:20:53Z |
| publishDate | 2024 |
| publisher | Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1143112025-01-14T01:37:25Z http://psasir.upm.edu.my/id/eprint/114311/ Improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular Malaysia Zaini, Farah Anishah Sulaima, Mohamad Fani Wan Abdul Razak, Intan Azmira Othman, Mohammad Lutfi Mokhlis, Hazlie 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 function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a new hybrid model based on the LSSVM optimized by the improved bacterial foraging optimization algorithm (IBFOA) for forecasting the short-term daily electricity load in Peninsular Malaysia. The IBFOA based on the sine cosine equation addresses the limitations of fixed chemotaxis constants in the original bacterial foraging optimization algorithm (BFOA), enhancing its exploration and exploitation capabilities. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using mean absolute percentage error (MAPE) as the objective function. The comparative analysis demonstrates the model, achieving the highest determination coefficient (R2) of 0.9880 and significantly reducing the average MAPE value by 28.36%, 27.72%, and 5.47% compared to the deep neural network (DNN), LSSVM, and LSSVM-BFOA, respectively. Additionally, IBFOA exhibits faster convergence times compared to BFOA, highlighting the practicality of LSSVM-IBFOA for short-term load forecasting. Multidisciplinary Digital Publishing Institute 2024-11-06 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/114311/1/114311.pdf Zaini, Farah Anishah and Sulaima, Mohamad Fani and Wan Abdul Razak, Intan Azmira and Othman, Mohammad Lutfi and Mokhlis, Hazlie (2024) Improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular Malaysia. Algorithms, 17 (11). art. no. 510. ISSN 1999-4893; eISSN: 1999-4893 https://www.mdpi.com/1999-4893/17/11/510 10.3390/a17110510 |
| spellingShingle | Zaini, Farah Anishah Sulaima, Mohamad Fani Wan Abdul Razak, Intan Azmira Othman, Mohammad Lutfi Mokhlis, Hazlie Improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular Malaysia |
| title | Improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular Malaysia |
| title_full | Improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular Malaysia |
| title_fullStr | Improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular Malaysia |
| title_full_unstemmed | Improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular Malaysia |
| title_short | Improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular Malaysia |
| title_sort | improved bacterial foraging optimization algorithm with machine learning-driven short-term electricity load forecasting: a case study in peninsular malaysia |
| url | http://psasir.upm.edu.my/id/eprint/114311/ http://psasir.upm.edu.my/id/eprint/114311/ http://psasir.upm.edu.my/id/eprint/114311/ http://psasir.upm.edu.my/id/eprint/114311/1/114311.pdf |