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

Authors

  • Dao Lam Missouri University of Science & Technology
  • Shuhui Li The University of Alabama
  • Donald Wunsch Missouri University of Science & Technology

DOI:

https://doi.org/10.15625/1813-9663/30/4/5510

Keywords:

Clustering, ELM, forecast, HMM, time series data.

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.

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Published

2014-12-30

How to Cite

[1]
D. Lam, S. Li, and D. Wunsch, “Hidden Markov model with information criteria clustering and extreme learning machine regression for wind forecasting”, JCC, vol. 30, no. 4, p. 361, Dec. 2014.

Issue

Section

Computer Science