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Wind power prediction using random vector functional link network with capuchin search algorithm
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DOI：10.1016/j.asej.2022.102095.png)
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Abstract
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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 eval-uation experiments using public datasets from four wind turbines located in France, using several eval-uation 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 pre-diction capability of the RVFL.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).
Keywords:
Wind power prediction
Time series forecasting
Random Vector Functional Link network
Capuchin search algorithm (CapSA)
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