Design of a combined system based on two-stage data preprocessing and multi-objective optimization for wind speed prediction

Abstract

Reliable wind speed forecasting is crucial for the operation of wind power systems, and many efforts have been made to develop methods for wind speed prediction. However, most of them ignored feature extraction from original data, leading to poor performance. In this study, a novel combined forecasting system is proposed based on a two-stage data preprocessing technique, three component forecasting models and a novel combination method of a multi-objective optimization algorithm to compensate for their shortcomings. Through the two-stage data preprocessing, the raw data is decomposed and reshaped to reduce noisy and chaotic disturbance, which improves the quality of data input. The forecasting module uses component forecasting and a combination strategy that takes advantages of each model to achieve both accurate and stable results. Four 10-min wind speed datasets are employed for experiments, and the results of deterministic and probabilistic forecasting indicate that the proposed system achieves optimal accuracy and robustness comparing with contrastive models. For point and interval forecasting, the system achieves 3.1112%, 4.7375%, 2.7459%, and 2.1110% mean absolute percent errors and 96.6667%, 100%, 97.3333%, and 98% interval coverage probabilities for spring, summer, autumn and winter dataset, respectively, connoting a considerable potential for application in wind power production.