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

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. | Journal of Computer Science and Cybernetics, V.30, N.4 (2014), 361–376 DOI: 10.15625/1813-9663/30/4/5510 HIDDEN MARKOV MODEL WITH INFORMATION CRITERIA CLUSTERING AND EXTREME LEARNING MACHINE REGRESSION FOR WIND FORECASTING DAO LAM1 , SHUHUI LI2 , AND DONALD WUNSCH1 1 Department of Electrical & Computer Engineering, Missouri University of Science & Technology; dlmg4,dwunsch@mst.edu 2 Department of Electrical & Computer Engineering, The University of Alabama; sli@eng.ua.edu 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. Keywords. Clustering, ELM, forecast, HMM, time series data. 1. INTRODUCTION The importance of time series data has established its analysis as a major research focus in many areas where such data appear. These data continue to accumulate, causing the computational requirement to increase continuously and rapidly. The percentage of wind power making up the nation’s total electrical power supply has increased quickly. Wind power is, however, known for its variability [1]. Better forecasting of wind time series is helpful to operate windmills and to integrate wind power into the grid [2, 3]. The simplest method of wind forecasting is the persistence method, where the wind speed at time ’t + ∆t’ is .