A novel ensemble probabilistic forecasting system for uncertainty in wind speed

Jianzhou Wang, Shuai Wang, Bo Zeng and Haiyan Lu

Applied Energy, 2022, vol. 313, issue C, No S0306261922002434

Abstract: The quantification of wind speed uncertainty is of great significance for real-time control of wind turbines and power grid dispatching. However, the intermittence and fluctuation of wind energy present great challenges in modeling its uncertainty; research in this field is limited. A quantile regression bi-directional long short-term memory network (QrBiLStm) and a novel ensemble probabilistic forecasting strategy are proposed in this study to explore ensemble probabilistic forecasting. To verify the reliability of the proposed ensemble probabilistic forecasting system, the uncertainties of wind speed at wind farms in China were modeled as a case study. The results of comparative experiments including 15 other models demonstrate the superiority of this ensemble probabilistic forecasting system in terms of sharpness while maintaining high interval coverage. More specifically, it was observed that the prediction interval coverage probability obtained by the proposed system is above 97%, and the sharpness is improved by at least 24.21% as compared with the commonly used single models. The proposed ensemble probabilistic forecasting system can accurately quantify the uncertainty of wind speed, and also reduce the operation cost of power systems by improving the efficiency of wind energy utilization.

Keywords: Wind speed forecasts; Multi-objective optimization algorithm; Deep learning; Ensemble probabilistic strategy; Forecast uncertainty (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (27)

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DOI: 10.1016/j.apenergy.2022.118796

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