Ensemble wind speed forecasting system based on optimal model adaptive selection strategy: Case study in China. (October 2022)

• Record Type: Journal Article
• Authors: Dong, Yuqi; Li, Jing; Liu, Zhenkun; Niu, Xinsong; Wang, Jianzhou
• Is Part Of: Sustainable energy technologies and assessments. Volume 53:Part B(2022)
• Publication Date: 2022-10
• Subjects: Wind speed forecasting -- Decomposition forecasting ensemble system -- Multi-objective bald eagle search -- Optimal model adaptive selection; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2022.102535
• Languages: English
• ISSNs: 2213-1388

Abstract

Abstract: Under the vision of global carbon neutrality and peaking, the vigorous development of easily accessible renewable energy sources, such as wind energy, is extremely important. However, achieving accurate and reliable forecasts remains a challenging problem owing to the irregularity and volatility of wind speed data. Existing forecasting modules based on point prediction ignore the limitations of a single forecasting algorithm, fail to capture the wind speed uncertainty in data or achieve high forecasting accuracy. To overcome these shortcomings, an ensemble system of decomposition, adaptive selection, and forecast was proposed to simulate the actual wind speed data of a wind farm. Empirical analyses show that an excellent denoising effect is achieved by the data decomposition method. Moreover, the adaptability of forecasting modules to complex data is improved by the optimal submodel selection strategy. The reduction coefficients are effectively optimized by the multi-objective bald eagle search algorithm used for the first time. This system not only improves the point forecasting accuracy but also analyzes the uncertainty of wind speed, providing actual technical support for grid dispatching.