Optimal wind energy generation considering climatic variables by Deep Belief network (DBN) model based on modified coot optimization algorithm (MCOA). (October 2022)

• Record Type: Journal Article
• Authors: Wang, Hong-Yan; Chen, Bin; Pan, Dong; Lv, Zheng-Ang; Huang, Shu-Qin; Khayatnezhad, Majid; Jimenez, Giorgos
• Is Part Of: Sustainable energy technologies and assessments. Volume 53:Part C(2022)
• Publication Date: 2022-10
• Subjects: Wind energy -- Optimization -- Modified Coot Optimization Algorithm -- Climatic condition -- Energy consumption; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2022.102744
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: According to the increase of energy consumption, wind energy penetration in power generation systems, the wind energy uncertainty and also, due to complex and nonlinear relationships of climatic parameters such as wind speed, wind direction, temperature providing a model to the more accurate forecast of energy production is essential. In this study, an optimal Deep Belief Network (DBN) model has been represented to accurately predict wind energy. The DBN network model is optimized by Modified Coot Optimization Algorithm (MCOA). This procedure uses self-adaptive weighting technique and turbulent technique to prevent trapped local optimization. These two techniques solve the optimization problem, so that it will have a more accurate prediction than the original Coot Optimization Algorithm(COA). The analysis of the modified COA metaheuristic model shows the MCOA model has a minimum of a standard deviation compared to other metaheuristic algorithms. Therefore, the MCOA metaheuristic algorithm has maximum precision and maximum reliability. Also, the simulation of wind energy generation showed, the optimal DBN technique has the best simulation compared to other models. because this model utilizes two techniques self-adaptive weight and chaotic method to refine the weakness optimization process.