Wind power prediction using random vector functional link network with capuchin search algorithm
Wind power can be considered one of the most important green sources of electric power. The prediction of wind power is necessary to boost the power grid operations’ efficiency and increase power market competitiveness. Artificial neural networks (ANNs) are widely used in prediction applications, in...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447922004063 |
| _version_ | 1827808441643565056 |
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| author | Mohammed A.A. Al-qaness Ahmed A. Ewees Hong Fan Laith Abualigah Ammar H. Elsheikh Mohamed Abd Elaziz |
| author_facet | Mohammed A.A. Al-qaness Ahmed A. Ewees Hong Fan Laith Abualigah Ammar H. Elsheikh Mohamed Abd Elaziz |
| author_sort | Mohammed A.A. Al-qaness |
| collection | DOAJ |
| description | Wind power can be considered one of the most important green sources of electric power. The prediction of wind power is necessary to boost the power grid operations’ efficiency and increase power market competitiveness. Artificial neural networks (ANNs) are widely used in prediction applications, including wind power. The Random Vector Functional Link (RVFL) is an efficient ANN model that can be employed in time-series forecasting applications. However, the configuration process of the RVFL needs to be improved. Thus, in this paper, we presented an optimized RVFL network using a new naturally inspired technique called the Capuchin search algorithm (CapSA). The main function of the CapSA is to boost the configuration of the traditional RVFL and enhance its prediction capability. We implement extensive evaluation experiments using public datasets from four wind turbines located in France, using several evaluation measures called RMSE, MAE, MAPE, and R2. The evaluation outcomes reveal that the CapSA-RVFL obtained the best prediction accuracy compared to the original RVFL and several variants of the RVFL model, which verifies that the application of CapSA has a significant contribution to improving the prediction capability of the RVFL. |
| first_indexed | 2024-03-11T22:27:10Z |
| format | Article |
| id | doaj.art-c12be06bc0714e19be4c7ef492551284 |
| institution | Directory Open Access Journal |
| issn | 2090-4479 |
| language | English |
| last_indexed | 2024-03-11T22:27:10Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj.art-c12be06bc0714e19be4c7ef4925512842023-09-24T05:14:49ZengElsevierAin Shams Engineering Journal2090-44792023-09-01149102095Wind power prediction using random vector functional link network with capuchin search algorithmMohammed A.A. Al-qaness0Ahmed A. Ewees1Hong Fan2Laith Abualigah3Ammar H. Elsheikh4Mohamed Abd Elaziz5College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, ChinaDepartment of Information Systems, College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia; Department of Computer, Damietta University, Damietta, EgyptState Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; Corresponding author at: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China (H. Fan)Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan; Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan; Faculty of Information Technology, Middle East University, Amman 11831, Jordan; Applied science research center, Applied science private university, Amman 11931, Jordan; School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, MalaysiaDepartment of Production Engineering and Mechanical Design, Tanta University, Tanta 31527, EgyptDepartment of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt; Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, UAE.; Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon.Wind power can be considered one of the most important green sources of electric power. The prediction of wind power is necessary to boost the power grid operations’ efficiency and increase power market competitiveness. Artificial neural networks (ANNs) are widely used in prediction applications, including wind power. The Random Vector Functional Link (RVFL) is an efficient ANN model that can be employed in time-series forecasting applications. However, the configuration process of the RVFL needs to be improved. Thus, in this paper, we presented an optimized RVFL network using a new naturally inspired technique called the Capuchin search algorithm (CapSA). The main function of the CapSA is to boost the configuration of the traditional RVFL and enhance its prediction capability. We implement extensive evaluation experiments using public datasets from four wind turbines located in France, using several evaluation measures called RMSE, MAE, MAPE, and R2. The evaluation outcomes reveal that the CapSA-RVFL obtained the best prediction accuracy compared to the original RVFL and several variants of the RVFL model, which verifies that the application of CapSA has a significant contribution to improving the prediction capability of the RVFL.http://www.sciencedirect.com/science/article/pii/S2090447922004063Wind power predictionTime series forecastingRandom Vector Functional Link networkCapuchin search algorithm (CapSA) |
| spellingShingle | Mohammed A.A. Al-qaness Ahmed A. Ewees Hong Fan Laith Abualigah Ammar H. Elsheikh Mohamed Abd Elaziz Wind power prediction using random vector functional link network with capuchin search algorithm Ain Shams Engineering Journal Wind power prediction Time series forecasting Random Vector Functional Link network Capuchin search algorithm (CapSA) |
| title | Wind power prediction using random vector functional link network with capuchin search algorithm |
| title_full | Wind power prediction using random vector functional link network with capuchin search algorithm |
| title_fullStr | Wind power prediction using random vector functional link network with capuchin search algorithm |
| title_full_unstemmed | Wind power prediction using random vector functional link network with capuchin search algorithm |
| title_short | Wind power prediction using random vector functional link network with capuchin search algorithm |
| title_sort | wind power prediction using random vector functional link network with capuchin search algorithm |
| topic | Wind power prediction Time series forecasting Random Vector Functional Link network Capuchin search algorithm (CapSA) |
| url | http://www.sciencedirect.com/science/article/pii/S2090447922004063 |
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