Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer

Mohammed A. A. Al-qaness (), Ahmed A. Ewees, Mohamed Abd Elaziz and Ahmed H. Samak
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Mohammed A. A. Al-qaness: College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China
Ahmed A. Ewees: College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia
Mohamed Abd Elaziz: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Ahmed H. Samak: College of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia

Energies, 2022, vol. 15, issue 24, 1-14

Abstract: It is necessary to study different aspects of renewable energy generation, including wind energy. Wind power is one of the most important green and renewable energy resources. The estimation of wind energy generation is a critical task that has received wide attention in recent years. Different machine learning models have been developed for this task. In this paper, we present an efficient forecasting model using naturally inspired optimization algorithms. We present an optimized dendritic neural regression (DNR) model for wind energy prediction. A new variant of the seagull optimization algorithm (SOA) is developed using the search operators of the Aquila optimizer (AO). The main idea is to apply the operators of the AO as a local search in the traditional SOA, which boosts the SOA’s search capability. The new method, called SOAAO, is employed to train and optimize the DNR parameters. We used four wind speed datasets to assess the performance of the presented time-series prediction model, called DNR-SOAAO, using different performance indicators. We also assessed the quality of the SOAAO with extensive comparisons to the original versions of the SOA and AO, as well as several other optimization methods. The developed model achieved excellent results in the evaluation. For example, the SOAAO achieved high R 2 results of 0.95, 0.96, 0.95, and 0.91 on the four datasets.

Keywords: wind power; dendritic neural regression (DNR); seagull optimization algorithm; Aquila optimizer; metaheuristic; time series; forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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