A combined filtering strategy for short term and long term wind speed prediction with improved accuracy

Haoshu Cai, Xiaodong Jia, Jianshe Feng, Qibo Yang, Yuan-Ming Hsu, Yudi Chen and Jay Lee

Renewable Energy, 2019, vol. 136, issue C, 1082-1090

Abstract: Wind Speed (WS) prediction plays a more and more important role in the wind farm operation and maintenance. In current literature, the short term (<6 h ahead) and medium/long (>6 h ahead) WS prediction are normally provided by different models. The statistical models are found effective in short-term prediction while the Numerical Weather Prediction (NWP) model is important to ensure the medium/long-term prediction accuracy. Driven by the needs of enhanced predictor that is effective for multiple time scales, this paper proposes a novel filtering strategy which integrates the statistical predictors and the NWP model outputs into one unified framework. Based on the proposed filtering strategy, a combined predictor SVR + SDA + UKF (Support Vector Regression + Stacked De-noising Auto-encoder + Unscented Kalman Filter) is proposed and validated. In the proposed predictor, the SVR term propagates the state vector of UKF and ensures short-term prediction accuracy. The SDA term fuses the NWP model outputs and mainly contributes to medium/long-term prediction accuracy. Consequently, the proposed method achieves improved accuracy in both short and medium/long-term WS prediction. In the case studies, the effectiveness of the proposed filtering strategy and the superiority of the predictor are demonstrated by the real-world data collected from an off-shore wind farm.

Keywords: Wind speed prediction; Stacked de-noising auto-encoder; Unscented Kalman filter; Support vector machine; Forecasting (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:136:y:2019:i:c:p:1082-1090

DOI: 10.1016/j.renene.2018.09.080

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