Short-Term Wind Power Prediction Based on Empirical Mode Decomposition and Extreme Learning Machine

DOI

10.2991/emcpe-16.2016.173How to use a DOI?

Keywords

Wind Power; Power Prediction; Empirical Mode Decomposition; Extreme Learning Machine

Abstract

Wind power prediction is important for the power system with plenty of wind power. This paper studies the method combined with empirical mode decomposition and extreme learning machine for short-term wind power prediction. The empirical mode decomposition method is utilized to decompose the signal of wind power into sequences with different characteristic scale. The extreme learning machine method is used to model and predict each sequence. Eventually, the prediction results of each sequence are added to obtain the final wind-power prediction results. The simulation result shows that the proposed method in this study improves the prediction accuracy of wind power prediction.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jiajia Wu
AU  - Changliang Liu
PY  - 2016/08
DA  - 2016/08
TI  - Short-Term Wind Power Prediction Based on Empirical Mode Decomposition and Extreme Learning Machine
BT  - Proceedings of the 2016 5th International Conference on Environment, Materials, Chemistry and Power Electronics
PB  - Atlantis Press
SP  - 695
EP  - 700
SN  - 2352-5401
UR  - https://doi.org/10.2991/emcpe-16.2016.173
DO  - 10.2991/emcpe-16.2016.173
ID  - Wu2016/08
ER  -