A Hybrid Improved Whale Optimization Algorithm with Support Vector Machine for Short-Term Photovoltaic Power Prediction
Presently, the grid-connected scale from photovoltaic (PV) system is getting higher among renewable power generations. However, the PV output power can be affected by different meteorological conditions due to PV randomness and volatility. Accordingly, reasonable generation plans can be well arrange...
| প্রধান লেখক: | , , , , , |
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| বিন্যাস: | প্রবন্ধ |
| ভাষা: | English |
| প্রকাশিত: |
Taylor & Francis Group
2022-12-01
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| মালা: | Applied Artificial Intelligence |
| অনলাইন ব্যবহার করুন: | http://dx.doi.org/10.1080/08839514.2021.2014187 |
| _version_ | 1827774550923804672 |
|---|---|
| author | Bing Gao Haiyue Yang Hsiung-Cheng Lin Zhengping Wang Weipeng Zhang Hua Li |
| author_facet | Bing Gao Haiyue Yang Hsiung-Cheng Lin Zhengping Wang Weipeng Zhang Hua Li |
| author_sort | Bing Gao |
| collection | DOAJ |
| description | Presently, the grid-connected scale from photovoltaic (PV) system is getting higher among renewable power generations. However, the PV output power can be affected by different meteorological conditions due to PV randomness and volatility. Accordingly, reasonable generation plans can be well arranged using accurate PV power prediction among various types of energy sources, thus reducing the effect of PV system on the grid. To resolve this problem, a PV output power prediction model, namely IMWOASVM, is proposed based on the combination of improved whale optimization algorithm (IMWOA) and support vector machine (SVM). The IMWOA is used to optimize the kernel function parameter and penalty coefficient in SVM. The optimal parameter and coefficient values can then be input to SVM for enhancing the PV prediction. The performance results verify that the coefficient of determination using the IMWOA model can reach beyond 99% in both sunny and cloudy days. Simultaneously, the mean absolute errors on sunny and cloudy days are 0.0251 and 0.0705, respectively. The root mean square errors in sunny and cloudy days are 2.17% and 1.03%, respectively. The results confirm that the proposed model effectively increases the accuracy of the PV output power prediction and is superior to existing methods. |
| first_indexed | 2024-03-11T13:39:57Z |
| format | Article |
| id | doaj.art-042d793ff42c4f5d99ce9e4e73aac093 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-11T13:39:57Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-042d793ff42c4f5d99ce9e4e73aac0932023-11-02T13:36:37ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452022-12-0136110.1080/08839514.2021.20141872014187A Hybrid Improved Whale Optimization Algorithm with Support Vector Machine for Short-Term Photovoltaic Power PredictionBing Gao0Haiyue Yang1Hsiung-Cheng Lin2Zhengping Wang3Weipeng Zhang4Hua Li5Department of Discover and Plan, State Grid Hengshui Electric Power Supply CompanyState Grid Hengshui Electric Power Supply CompanyDepartment of Electronic Engineering, National Chin-Yi University of Technology, Taichung, TaiwanDepartment of Discover and Plan, State Grid Hengshui Electric Power Supply CompanyTianjin Chengxi District Power Supply CompanyHebei University of TechnologyPresently, the grid-connected scale from photovoltaic (PV) system is getting higher among renewable power generations. However, the PV output power can be affected by different meteorological conditions due to PV randomness and volatility. Accordingly, reasonable generation plans can be well arranged using accurate PV power prediction among various types of energy sources, thus reducing the effect of PV system on the grid. To resolve this problem, a PV output power prediction model, namely IMWOASVM, is proposed based on the combination of improved whale optimization algorithm (IMWOA) and support vector machine (SVM). The IMWOA is used to optimize the kernel function parameter and penalty coefficient in SVM. The optimal parameter and coefficient values can then be input to SVM for enhancing the PV prediction. The performance results verify that the coefficient of determination using the IMWOA model can reach beyond 99% in both sunny and cloudy days. Simultaneously, the mean absolute errors on sunny and cloudy days are 0.0251 and 0.0705, respectively. The root mean square errors in sunny and cloudy days are 2.17% and 1.03%, respectively. The results confirm that the proposed model effectively increases the accuracy of the PV output power prediction and is superior to existing methods.http://dx.doi.org/10.1080/08839514.2021.2014187 |
| spellingShingle | Bing Gao Haiyue Yang Hsiung-Cheng Lin Zhengping Wang Weipeng Zhang Hua Li A Hybrid Improved Whale Optimization Algorithm with Support Vector Machine for Short-Term Photovoltaic Power Prediction Applied Artificial Intelligence |
| title | A Hybrid Improved Whale Optimization Algorithm with Support Vector Machine for Short-Term Photovoltaic Power Prediction |
| title_full | A Hybrid Improved Whale Optimization Algorithm with Support Vector Machine for Short-Term Photovoltaic Power Prediction |
| title_fullStr | A Hybrid Improved Whale Optimization Algorithm with Support Vector Machine for Short-Term Photovoltaic Power Prediction |
| title_full_unstemmed | A Hybrid Improved Whale Optimization Algorithm with Support Vector Machine for Short-Term Photovoltaic Power Prediction |
| title_short | A Hybrid Improved Whale Optimization Algorithm with Support Vector Machine for Short-Term Photovoltaic Power Prediction |
| title_sort | hybrid improved whale optimization algorithm with support vector machine for short term photovoltaic power prediction |
| url | http://dx.doi.org/10.1080/08839514.2021.2014187 |
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