Short-Term Power Load Forecasting Based on DPSO-LSSVM Model
The accurate prediction of short-term power load is a critical element for maintaining the normal and stable operation of the power system. For short-term power load forecasting, the collected power load data is preprocessed to quantify temperature, weather, and date types. A short-term load forecas...
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| Vydané v: | IEEE access Ročník 13; s. 32211 - 32224 |
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| Hlavní autori: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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IEEE
2025
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| ISSN: | 2169-3536, 2169-3536 |
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| Abstract | The accurate prediction of short-term power load is a critical element for maintaining the normal and stable operation of the power system. For short-term power load forecasting, the collected power load data is preprocessed to quantify temperature, weather, and date types. A short-term load forecasting model based on least squares support vector machine is constructed, and the optimal parameters of the model are established. The dynamic particle swarm optimization algorithm is utilized to dynamically adjust the parameters to achieve higher accuracy in load forecasting. The findings denoted that the average absolute percentage error of the least squares support vector machine model using linear kernel function is only 3.75%, the average absolute error is only 256.38MW, and the root mean square error is only 311.20MW. The mean absolute percentage error of the proposed model is only 1.91%, significantly lower than other advanced models. The developed model has stronger adaptability and higher prediction accuracy in dealing with the complexity and dynamic changes of power load data, providing effective technical support for the operation optimization and decision-making of the power system. |
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| AbstractList | The accurate prediction of short-term power load is a critical element for maintaining the normal and stable operation of the power system. For short-term power load forecasting, the collected power load data is preprocessed to quantify temperature, weather, and date types. A short-term load forecasting model based on least squares support vector machine is constructed, and the optimal parameters of the model are established. The dynamic particle swarm optimization algorithm is utilized to dynamically adjust the parameters to achieve higher accuracy in load forecasting. The findings denoted that the average absolute percentage error of the least squares support vector machine model using linear kernel function is only 3.75%, the average absolute error is only 256.38MW, and the root mean square error is only 311.20MW. The mean absolute percentage error of the proposed model is only 1.91%, significantly lower than other advanced models. The developed model has stronger adaptability and higher prediction accuracy in dealing with the complexity and dynamic changes of power load data, providing effective technical support for the operation optimization and decision-making of the power system. |
| Author | Ling, Bu Zhang, Linhao Wei, Tao Li, Jiadong Xu, Jinglong Wang, Jinteng Wu, Zuoping Ji, Shujun |
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| Cites_doi | 10.1109/TVT.2023.3247729 10.1109/JAS.2023.123387 10.1109/TPWRS.2023.3271325 10.1109/tsg.2021.3091469 10.1109/TSG.2023.3264525 10.1109/JAS.2023.123531 10.1109/tii.2020.2990962 10.1109/tsg.2021.3093515 10.1109/JSEN.2023.3269064 10.1109/TPWRS.2020.3042389 10.1109/TLA.2023.10268274 10.1109/TPEL.2020.2967053 10.1109/TSTE.2023.3250710 10.1109/tpwrd.2022.3178822 10.1109/TII.2022.3230726 10.1109/TETC.2023.3268182 10.1109/TEVC.2023.3258491 10.1109/TCYB.2021.3088519 10.1109/TPWRS.2023.3257353 10.1109/TNET.2023.3276363 10.1109/TII.2020.3000184 10.1109/TVT.2023.3275959 10.1109/tpwrs.2020.3028133 10.1109/TSG.2023.3266342 10.1109/TIA.2023.3276356 10.1109/TII.2023.3259445 10.1109/TIA.2023.3289440 10.1109/TPWRS.2023.3256130 10.47852/bonviewJDSIS32021078 10.1109/TSG.2023.3268633 10.1109/TSTE.2023.3274939 10.1109/JSEN.2023.3237876 |
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| SubjectTerms | Accuracy Adaptation models Data models dynamic particle swarm optimization algorithm Heuristic algorithms Load modeling LSSVM Mathematical models Optimization power load Power system dynamics Prediction algorithms Predictive models radial basis kernel function short-term |
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| Title | Short-Term Power Load Forecasting Based on DPSO-LSSVM Model |
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