A combined filtering strategy for short term and long term wind speed prediction with improved accuracy. (June 2019) - Cai, Haoshu; Jia, Xiaodong; Feng, Jianshe; Yang, Qibo; Hsu, Yuan-Ming; Chen, Yudi; Lee, Jay
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. Highlights: A combined wind speed prediction strategy based on Autoencoder and UKF is proposed. The method improves accuracy both in short term and long term predictions. The result is validated on two months' SCADA data from a real wind farm.