A Broad Learning System with Ensemble and Classification Methods for Multi-step-ahead Wind Speed Prediction

Abstract

Short-term wind speed prediction plays a significant role in the management of large-scale wind power plants. However, wind speed prediction is extremely complex and difficult due to the volatility and non-linearity of wind. For this purpose, a broad learning system (BLS) with ensemble and classification named BLS-EC is proposed to predict multi-step-ahead wind speed. The proposed method is based on a new neural network termed the BLS, which could work out the complex non-linear relation by learning model while ensuring the computational efficiency. To overcome the randomness and instability of a single BLS, this paper proposes the BLS ensemble method to improve the generalization and stability of the network. In order to improve the accuracy of prediction, a method called classification-guided regression is proposed to distinguish different variation patterns of initial predicted wind speed. According to the classification result, different pattern sequences are re-predicted to obtain the final prediction result. Applying this thinking and method into research of three real-time wind speed datasets which were taken from Sotavento Galicia SA (SG), Alberta (ALB), and Newfoundland (NFL), the validity and practical value of this method can be demonstrated. Results obtained clearly show that BLS is better than existing methods ARIMA and RBF. Moreover, the BLS-EC method improved generalization performance and the predicting precision of a single BLS. In this study, the BLS-EC was proposed and successfully applied to wind speed prediction.