Application of improved version of multi verse optimizer algorithm for modeling solar radiation
For better estimation of renewable environmental friendly and carbon-free energy resources, precise prediction of solar energy is very essential. However, accurate prediction of solar energy is a challenging task due to its fluctuations and due to climatic factors those make it very nonlinear in nat...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722017383 |
| _version_ | 1797901854638604288 |
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| author | Rana Muhammad Adnan Ikram Hong-Liang Dai Ahmed A. Ewees Jalal Shiri Ozgur Kisi Mohammad Zounemat-Kermani |
| author_facet | Rana Muhammad Adnan Ikram Hong-Liang Dai Ahmed A. Ewees Jalal Shiri Ozgur Kisi Mohammad Zounemat-Kermani |
| author_sort | Rana Muhammad Adnan Ikram |
| collection | DOAJ |
| description | For better estimation of renewable environmental friendly and carbon-free energy resources, precise prediction of solar energy is very essential. However, accurate prediction of solar energy is a challenging task due to its fluctuations and due to climatic factors those make it very nonlinear in nature. Therefore, in this study, the novel robust soft computing method is applied to predict solar radiation of two stations located in the southeast region of China. For modeling solar radiation of selected stations, the improved version of multi verse optimizer algorithm (IMVO) is utilized with integration of least square support vector machine (LSSVM) for better tuning the hyperparameters of LSSVM model. For validation, the newly developed method is compared with other algorithms integrated with LSSVM models, such as LSSVM with genetic algorithm (LSSVM-GE), LSSVM with gray wolf optimization (LSSVM-GWO), LSSVM with sine–cosine algorithm (LSSVM-CSA) and LSSVM with multi verse algorithm original version (LSSVM-MVO). It is found that newly developed method, LSSVM-IMVO, provided more accurate results in comparison to other models. For better visualization of data and model application, three different training testing data splitting strategies are used. It is found that the increase in training sample size considerably improved the models’ accuracies. |
| first_indexed | 2024-04-10T09:08:31Z |
| format | Article |
| id | doaj.art-2be5018d43f5441f94f4b8745ce96883 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T09:08:31Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-2be5018d43f5441f94f4b8745ce968832023-02-21T05:13:19ZengElsevierEnergy Reports2352-48472022-11-0181206312080Application of improved version of multi verse optimizer algorithm for modeling solar radiationRana Muhammad Adnan Ikram0Hong-Liang Dai1Ahmed A. Ewees2Jalal Shiri3Ozgur Kisi4Mohammad Zounemat-Kermani5School of Economics and Statistics, Guangzhou University, Guangzhou 510006, ChinaSchool of Economics and Statistics, Guangzhou University, Guangzhou 510006, China; Corresponding author.Department of Computer, Damietta University, Damietta 34517, EgyptFaculty of Agriculture, Water Engineering Department, University of Tabriz, Tabriz, IranDepartment of Civil Engineering, Technical University of Lübeck, 23562, Lübeck, Germany; School of Technology, Ilia State University, 0162, Tbilisi, Georgia; Corresponding author at: Department of Civil Engineering, Technical University of Lübeck, 23562, Lübeck, Germany.Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, IranFor better estimation of renewable environmental friendly and carbon-free energy resources, precise prediction of solar energy is very essential. However, accurate prediction of solar energy is a challenging task due to its fluctuations and due to climatic factors those make it very nonlinear in nature. Therefore, in this study, the novel robust soft computing method is applied to predict solar radiation of two stations located in the southeast region of China. For modeling solar radiation of selected stations, the improved version of multi verse optimizer algorithm (IMVO) is utilized with integration of least square support vector machine (LSSVM) for better tuning the hyperparameters of LSSVM model. For validation, the newly developed method is compared with other algorithms integrated with LSSVM models, such as LSSVM with genetic algorithm (LSSVM-GE), LSSVM with gray wolf optimization (LSSVM-GWO), LSSVM with sine–cosine algorithm (LSSVM-CSA) and LSSVM with multi verse algorithm original version (LSSVM-MVO). It is found that newly developed method, LSSVM-IMVO, provided more accurate results in comparison to other models. For better visualization of data and model application, three different training testing data splitting strategies are used. It is found that the increase in training sample size considerably improved the models’ accuracies.http://www.sciencedirect.com/science/article/pii/S2352484722017383Solar radiation predictionLeast square support vector machineGenetic algorithmGray wolf optimizationSine–cosine algorithmMulti-verse optimizer |
| spellingShingle | Rana Muhammad Adnan Ikram Hong-Liang Dai Ahmed A. Ewees Jalal Shiri Ozgur Kisi Mohammad Zounemat-Kermani Application of improved version of multi verse optimizer algorithm for modeling solar radiation Energy Reports Solar radiation prediction Least square support vector machine Genetic algorithm Gray wolf optimization Sine–cosine algorithm Multi-verse optimizer |
| title | Application of improved version of multi verse optimizer algorithm for modeling solar radiation |
| title_full | Application of improved version of multi verse optimizer algorithm for modeling solar radiation |
| title_fullStr | Application of improved version of multi verse optimizer algorithm for modeling solar radiation |
| title_full_unstemmed | Application of improved version of multi verse optimizer algorithm for modeling solar radiation |
| title_short | Application of improved version of multi verse optimizer algorithm for modeling solar radiation |
| title_sort | application of improved version of multi verse optimizer algorithm for modeling solar radiation |
| topic | Solar radiation prediction Least square support vector machine Genetic algorithm Gray wolf optimization Sine–cosine algorithm Multi-verse optimizer |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722017383 |
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