A fuzzy intelligent forecasting system based on combined fuzzification strategy and improved optimization algorithm for renewable energy power generation

Abstract

• Develop a novel hybrid forecasting system focusing on small sample forecasting. • Forecast the renewable energy power generation of different regions in the world. • Improve the fuzzification ability by combined strategy and hesitant fuzzy sets. • Search the optimal aggregated weights based on improved optimization algorithm. • Introduce the chaotic map into the equilibrium optimization algorithm. Renewable energy power generation has significant potential to electricity supply sector with great sense to greenhouse gas control. Thus, it is vital to develop an effective forecasting model for renewable energy power generation forecasting which can provide a reference and basis for power generation planning and the energy strategic deployment. However, influenced by the complex data characteristic and sample size limitation, the application of some traditional forecasting models is restricted with poor forecasting performance. In this paper, a novel fuzzy time series forecasting based on combined fuzzification strategy and improved optimization algorithm is proposed for renewable energy power generation forecasting. The hesitant fuzzy sets are applied to deal with the combined fuzzification strategy and the improved optimization algorithm is developed to optimize the aggregate weights of the hesitant fuzzy sets. The experimental analysis and discussion all demonstrated the excellent performance of the proposed forecasting system in small sample forecasting for renewable energy power generation forecasting.