Natural phase space reconstruction-based broad learning system for short-term wind speed prediction: Case studies of an offshore wind farm

Abstract

Accurate prediction of wind speed can not only help to develop strategies for wind turbine operation, but also reduce impact on power systems when wind energy is integrated into the grid. However, it is difficult to predict speed accurately due to the stochastic nature of wind. To address this issue, this paper presents a novel wind speed prediction model based on phase space reconstruction and broad learning system (BLS). First, phase spaces under various delay dimensions and phase scales are reconstructed. Afterwards, natural neighbor spectrum is constructed without parameter setting based on phase vectors for selecting the optimal phase space. Then, the optimal inputting number of BLS is decided, elastic-net regularization is introduced to alleviate overfitting and BLS is trained in an incremental way. Finally, predicting values are given by output layer. Two cases about an offshore wind farm are used to demonstrate the effectiveness of the proposed model and five traditional models are used for comparison. Results show that compared with the other models, proposed model not only achieves higher predicting accuracy, but also has faster learning speed, meeting the requirement of online prediction for scale-growing wind speed and leaving more time for making strategies about grid planning.