Wind speed forecasting based on model selection, fuzzy cluster, and multi-objective algorithm and wind energy simulation by Betz's theory

• Published in: Expert systems with applications Vol. 193; p. 116509
• Main Authors: Zhang, Shenghui, Wang, Chen, Liao, Peng, Xiao, Ling, Fu, Tonglin
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.05.2022; Elsevier BV
• Subjects: Algorithms; Alternative energy sources; Betz's theory; Clean energy; Computer simulation; Data mining; Forecasting; Fuzzy c-means cluster; Mathematical models; Model selection strategy; Modified multi-objective algorithm; Modules; Neural networks; Renewable energy sources; Simulation; Time series; Wind farms; Wind power; Wind power generation; Wind speed; Wind speed forecasting; Wind turbines; Model selection strategy; Fuzzy c-means cluster; Betz's theory; Modified multi-objective algorithm; Wind speed forecasting
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.116509

•A wind speed forecasting and wind energy simulation management system developed.•A robust novel hybrid system based on three modular is proposed.•A multi-objective optimization algorithm is developed to optimize the parameters.•The model selection module searches the optimal forecasting values.•The conversion of wind power to electric energy had been discussed. Wind energy is of increasing interest to wind farm administrators as a clean and renewable energy source. Accurate wind speed forecasting and effective wind energy simulation can increase the capability of wind power combined with a grid and decrease the operating cost of wind farms. However, many previous studies have been restricted to wind speed forecasting, ignoring wind energy simulations. Thus, grid management cannot effectively estimate the power production of wind farms and leads to an increase in the abandonment wind rate in wind farms. In this study, a wind farm auxiliary management system is developed, which includes two modules: wind speed forecasting and wind energy simulation. In the wind speed forecasting module, first, a data mining algorithm is used to analyze different features of wind speed time series data in a wind farm. Subsequently, a feature selection algorithm is used to determine the representative wind speed time series of the wind farm, and it is combined with a data preprocessing method to effectively eliminate the noise of the original wind speed time series. Second, six hybrid neural network forecasting models based on a modified multi-objective algorithm are established to forecast wind speed. Finally, they are combined with a model selection strategy to yield the best forecasting value for each time point. In the wind energy simulation module, using Betz's theory, the physical transformation process of a wind turbine is estimated to determine the range of wind power generation.