Dynamic Time Wrapper Based Local Predictor for Wind Speed Prediction

The quality of training sample is an important factor for wind speed prediction using data‐driven approaches, such as deep learning. This paper proposes a novel local predictor based on dynamic time wrapper (DTW) as training sample adaptation for wind speed prediction. After analyzing the similarity of wind speed time series using dynamic time wrapper, a local predictor is applied to improve the quality of training sample. An evaluation index is firstly proposed for estimating the quality of selected training sample. To verify the effectiveness of the proposed method, a random forest model with local predictor based on dynamic time warping (RF‐DTWLP) is applied to predict the wind speed. The simulation results demonstrate the prediction of wind speed by local predictor based on DTW has higher prediction accuracy. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.