A hybrid prediction model for short-term load forecasting in power systems
Short-term load forecasting (STLF) plays a vital role in effective power system management by assisting power dispatch centers in developing generation plans and ensuring smooth system operation. This study introduces a novel hybrid prediction model called iSSA-LSSVM to tackle the STLF challenge. By...
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| Format: | Article |
| Language: | English |
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ECTI Association
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/44084/ http://umpir.ump.edu.my/id/eprint/44084/1/A%20hybrid%20prediction%20model%20for%20short-term.pdf |
| _version_ | 1848827028117127168 |
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| author | Zuriani, Mustaffa Mohd Herwan, Sulaiman |
| author_facet | Zuriani, Mustaffa Mohd Herwan, Sulaiman |
| author_sort | Zuriani, Mustaffa |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Short-term load forecasting (STLF) plays a vital role in effective power system management by assisting power dispatch centers in developing generation plans and ensuring smooth system operation. This study introduces a novel hybrid prediction model called iSSA-LSSVM to tackle the STLF challenge. By integrating the Salp Swarm Algorithm (SSA) with Least Squares Support Vector Machines (LSSVM), the iSSA-LSSVM model significantly improves LSSVM's prediction accuracy. One of the key contributions is the model's ability to autonomously ne-tune LSSVM hyperparameters, eliminating the need for manual adjustments and optimizing performance. Modifying the SSA within iSSA-LSSVM enhances the original algorithm's exploration and exploitation capabilities, ensuring better search efficiency and precision. Using a dataset with four independent variables as input and electrical power output as the target variable, the model demonstrates superior predictive performance. Comparative analysis with three other models shows that iSSA-LSSVM achieves a lower Mean Square Error (MSE) and faster convergence. This improvement in accuracy and efficiency enhances STLF, allowing power dispatch centers to develop more precise generation plans and ensure more reliable power system operation. |
| first_indexed | 2025-11-15T03:54:12Z |
| format | Article |
| id | ump-44084 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:54:12Z |
| publishDate | 2024 |
| publisher | ECTI Association |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-440842025-03-14T07:31:56Z http://umpir.ump.edu.my/id/eprint/44084/ A hybrid prediction model for short-term load forecasting in power systems Zuriani, Mustaffa Mohd Herwan, Sulaiman TK Electrical engineering. Electronics Nuclear engineering Short-term load forecasting (STLF) plays a vital role in effective power system management by assisting power dispatch centers in developing generation plans and ensuring smooth system operation. This study introduces a novel hybrid prediction model called iSSA-LSSVM to tackle the STLF challenge. By integrating the Salp Swarm Algorithm (SSA) with Least Squares Support Vector Machines (LSSVM), the iSSA-LSSVM model significantly improves LSSVM's prediction accuracy. One of the key contributions is the model's ability to autonomously ne-tune LSSVM hyperparameters, eliminating the need for manual adjustments and optimizing performance. Modifying the SSA within iSSA-LSSVM enhances the original algorithm's exploration and exploitation capabilities, ensuring better search efficiency and precision. Using a dataset with four independent variables as input and electrical power output as the target variable, the model demonstrates superior predictive performance. Comparative analysis with three other models shows that iSSA-LSSVM achieves a lower Mean Square Error (MSE) and faster convergence. This improvement in accuracy and efficiency enhances STLF, allowing power dispatch centers to develop more precise generation plans and ensure more reliable power system operation. ECTI Association 2024-10-12 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/44084/1/A%20hybrid%20prediction%20model%20for%20short-term.pdf Zuriani, Mustaffa and Mohd Herwan, Sulaiman (2024) A hybrid prediction model for short-term load forecasting in power systems. ECTI Transactions on Computer and Information Technology, 18 (4). pp. 568-578. ISSN 2286-9131. (Published) https://doi.org/10.37936/ecti-cit.2024184.257667 https://doi.org/10.37936/ecti-cit.2024184.257667 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Zuriani, Mustaffa Mohd Herwan, Sulaiman A hybrid prediction model for short-term load forecasting in power systems |
| title | A hybrid prediction model for short-term load forecasting in power systems |
| title_full | A hybrid prediction model for short-term load forecasting in power systems |
| title_fullStr | A hybrid prediction model for short-term load forecasting in power systems |
| title_full_unstemmed | A hybrid prediction model for short-term load forecasting in power systems |
| title_short | A hybrid prediction model for short-term load forecasting in power systems |
| title_sort | hybrid prediction model for short-term load forecasting in power systems |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/44084/ http://umpir.ump.edu.my/id/eprint/44084/ http://umpir.ump.edu.my/id/eprint/44084/ http://umpir.ump.edu.my/id/eprint/44084/1/A%20hybrid%20prediction%20model%20for%20short-term.pdf |