Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy Forecasting
This study presents a novel Fractional Whale Optimization Algorithm-Enhanced Support Vector Regression (FWOA-SVR) framework for solar energy forecasting, addressing the limitations of traditional SVR in modeling complex relationships within data. The proposed framework incorporates fractional calcul...
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MDPI AG
2025-01-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/9/1/35 |
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| author | Abdul Wadood Hani Albalawi Aadel Mohammed Alatwi Hafeez Anwar Tariq Ali |
| author_facet | Abdul Wadood Hani Albalawi Aadel Mohammed Alatwi Hafeez Anwar Tariq Ali |
| author_sort | Abdul Wadood |
| collection | DOAJ |
| description | This study presents a novel Fractional Whale Optimization Algorithm-Enhanced Support Vector Regression (FWOA-SVR) framework for solar energy forecasting, addressing the limitations of traditional SVR in modeling complex relationships within data. The proposed framework incorporates fractional calculus in the Whale Optimization Algorithm (WOA) to improve the balance between exploration and exploitation during hyperparameter tuning. The FWOA-SVR model is comprehensively evaluated against traditional SVR, Long Short-Term Memory (LSTM), and Backpropagation Neural Network (BPNN) models using training, validation, and testing datasets. Experimental results show that FWOA-SVR achieves superior performance with the lowest MSE values (0.036311, 0.03942, and 0.03825), RMSE values (0.19213, 0.19856, and 0.19577), and the highest R<sup>2</sup> values (0.96392, 0.96104, and 0.96192) for training, validation, and testing, respectively. These results highlight the significant improvements of FWOA-SVR in prediction accuracy and efficiency, surpassing benchmark models in capturing complex patterns within the data. The findings highlight the effectiveness of integrating fractional optimization techniques into machine learning frameworks for advancing solar energy forecasting solutions. |
| format | Article |
| id | doaj-art-95357ff99ceb42809617099dc939aff1 |
| institution | Kabale University |
| issn | 2504-3110 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj-art-95357ff99ceb42809617099dc939aff12025-01-24T13:33:27ZengMDPI AGFractal and Fractional2504-31102025-01-01913510.3390/fractalfract9010035Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy ForecastingAbdul Wadood0Hani Albalawi1Aadel Mohammed Alatwi2Hafeez Anwar3Tariq Ali4Renewable Energy and Environmental Technology Center, University of Tabuk, Tabuk 47913, Saudi ArabiaRenewable Energy and Environmental Technology Center, University of Tabuk, Tabuk 47913, Saudi ArabiaRenewable Energy and Environmental Technology Center, University of Tabuk, Tabuk 47913, Saudi ArabiaDepartment of Computer Science, National University of Computer and Emerging Science (NUCES-FAST), Peshawar 25100, PakistanArtificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk 47913, Saudi ArabiaThis study presents a novel Fractional Whale Optimization Algorithm-Enhanced Support Vector Regression (FWOA-SVR) framework for solar energy forecasting, addressing the limitations of traditional SVR in modeling complex relationships within data. The proposed framework incorporates fractional calculus in the Whale Optimization Algorithm (WOA) to improve the balance between exploration and exploitation during hyperparameter tuning. The FWOA-SVR model is comprehensively evaluated against traditional SVR, Long Short-Term Memory (LSTM), and Backpropagation Neural Network (BPNN) models using training, validation, and testing datasets. Experimental results show that FWOA-SVR achieves superior performance with the lowest MSE values (0.036311, 0.03942, and 0.03825), RMSE values (0.19213, 0.19856, and 0.19577), and the highest R<sup>2</sup> values (0.96392, 0.96104, and 0.96192) for training, validation, and testing, respectively. These results highlight the significant improvements of FWOA-SVR in prediction accuracy and efficiency, surpassing benchmark models in capturing complex patterns within the data. The findings highlight the effectiveness of integrating fractional optimization techniques into machine learning frameworks for advancing solar energy forecasting solutions.https://www.mdpi.com/2504-3110/9/1/35machine learningprediction modelswhale optimizationfractional calculusFractional Whale Optimization Algorithm-Enhanced Support Vector Regressionphotovoltaic |
| spellingShingle | Abdul Wadood Hani Albalawi Aadel Mohammed Alatwi Hafeez Anwar Tariq Ali Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy Forecasting Fractal and Fractional machine learning prediction models whale optimization fractional calculus Fractional Whale Optimization Algorithm-Enhanced Support Vector Regression photovoltaic |
| title | Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy Forecasting |
| title_full | Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy Forecasting |
| title_fullStr | Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy Forecasting |
| title_full_unstemmed | Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy Forecasting |
| title_short | Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy Forecasting |
| title_sort | design of a novel fractional whale optimization enhanced support vector regression fwoa svr model for accurate solar energy forecasting |
| topic | machine learning prediction models whale optimization fractional calculus Fractional Whale Optimization Algorithm-Enhanced Support Vector Regression photovoltaic |
| url | https://www.mdpi.com/2504-3110/9/1/35 |
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