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

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.