An improved GM(1,1) forecasting model based on Aquila Optimizer for wind power generation in Sichuan Province

Abstract With the rapid development of China's economy, the development of wind resources has important practical significance for alleviating environmental pollution problems in various provinces and cities in China. As China's clean energy province and t western economic center, Sichuan's wind power industry is gradually starting from the 13th Five-Year Plan. Considering the volatility and periodic characteristics of wind power generation in Sichuan Province, this paper proposes an optimized seasonal grey model based on a novel algorithm (Aquila Optimizer). We use dummy variables 1 and 0 to represent seasonal factors and perform seasonal classification of the sample data. According to the classification sequence, we construct a grey prediction model with optimized initial value and background value by Aquila Optimizer. In this paper, we use this model to predict wind power generation in Sichuan Province and verify the validity and rationality of the model by comparing it with other methods. Finally, we further predicted the power generation during the 14th Five-Year Plan period to provide policy advice and planning for the future development of the clean energy industry in Sichuan Province.