Hidden Markov model with information criteria clustering and extreme learning machine regression for wind forecasting

Abstract

This paper proposes a procedural pipeline for wind forecasting based on clustering and regression. First, the data are clustered into groups sharing  similar dynamic properties. Then, data in the same cluster are used to train the neural network that predicts wind speed. For clustering, a hidden Markov model (HMM) and the modified Bayesian information criteria (BIC) are incorporated in a new method of clustering time series data.  To forecast wind, a new method for wind time series data forecasting is developed based on the extreme learning machine (ELM). The clustering results improve the accuracy of the proposed method of wind forecasting. Experiments on a real dataset collected from various locations confirm the method's accuracy and capacity in the handling of a large amount  of data.