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

Hufang Yang, Ping Jiang, Ying Wang and Hongmin Li

Applied Energy, 2022, vol. 325, issue C, No S0306261922011175

Abstract: 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.

Keywords: Renewable energy power generation; Fuzzy time series; Combined fuzzification strategy; Improved optimization algorithm (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.apenergy.2022.119849

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