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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2016 Vol.6(1): 52-56 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2016.6.1.571

Stacked-Locally Weighted Ensemble Learning for Wind Speed Prediction

Hong Shi, Man Yu, and Qinghua Hu
Abstract—It is a key problem to reach a balance between wind power supply and demand in wind power grid. However, the balance is difficult to achieve due to the random and unstable characteristics of wind speed. The accurate wind speed and power forecast model is needed to be developed. In this paper, we design a stacked-locally weighted ensemble method (SLWE) for wind speed prediction based on two facts, wind speeds are typical of the time series; the neighboring wind speeds are more relevant. The previous time prediction is treated as one attribute in the prediction of next time wind speed. The architecture of SLWE not only considers the diversity but also the accuracy. The proposed method is tested on wind speed datasets from Jilin, China. Experimental results show the effectiveness of SLWE.

Index Terms—Ensemble learning, local learning, stacked, wind speed prediction.

Hong Shi and Man Yu are with the School of Computer Science and Technology, Tianjin University, Tianjin, China (e-mail: 120888349@qq.com, alxyu@tju.edu.cn).
Qinghua Hu is with Harbin Institute of Technology, Harbin, China (e-mail: qinghuahu@tju.edu.cn).


Cite: Hong Shi, Man Yu, and Qinghua Hu, "Stacked-Locally Weighted Ensemble Learning for Wind Speed Prediction," International Journal of Machine Learning and Computing vol.6, no. 1, pp. 52-56, 2016.

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