Short-term Wind Power Forecast Based on GA-Elman Neural Network and Nonlinear Combination Model

DOI

10.2991/ipemec-15.2015.178How to use a DOI?

Keywords

Elman neural network; GA; SVM; nonlinear combination model.

Abstract

The accuracy of short-term wind power forecast is important to the operation of power system. Based on the real-time wind power data, a wind power prediction model using Elman neural network is proposed. In order to overcome such disadvantages of Elman neural network as easily falling into local minimum, this paper put forward using Genetic algorithm (GA) to optimize the weight and threshold of Elman neural network. At the same time, it’s advisable to use Support Vector Machine (SVM) to comparatively do prediction and put two outcomes as input vector for generalized regression neural network (GRNN) to do nonlinear combination forecasting. By analyzing the measured data of wind farms, indicate that the nonlinear combination of forecasting model can improve forecast accuracy.

Copyright

© 2015, 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  - Pengyu Zhang
PY  - 2015/05
DA  - 2015/05
TI  - Short-term Wind Power Forecast Based on GA-Elman Neural Network and Nonlinear Combination Model
BT  - Proceedings of the 2015 International Power, Electronics and Materials Engineering Conference
PB  - Atlantis Press
SP  - 968
EP  - 973
SN  - 2352-5401
UR  - https://doi.org/10.2991/ipemec-15.2015.178
DO  - 10.2991/ipemec-15.2015.178
ID  - Zhang2015/05
ER  -