A Novel Wind Power Capacity Combined Forecasting method based on Backtracking Search Algorithm

DOI

10.2991/iiicec-15.2015.162How to use a DOI?

Keywords

Wind capacity forecasting; Differential evolution algorithm; support vector regression

Abstract

As wind power is a mature and important renewable energy, wind power capacity forecasting plays an important role in renewable energy generation’s plan, investment and operation. Combined model is an effective load forecasting method; however, how to determine the weights is a hot issue. This paper proposed a combined model with backtracking search algorithm for optimizing weights, which can improve the performance of each single forecasting model of regression, BPNN and SVM. In order to prove the effectiveness of the proposed model, an application of the China’s wind power capacity from 2001 to 2013 was evaluated. The experiment results show that the proposed model gets the maximum mean absolute percentage error (MAPE) value 4.72%, which is better than the results of regression, BPNN and SVM.

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/).

Download article (PDF)

TY  - CONF
AU  - JunChao Yang
PY  - 2015/03
DA  - 2015/03
TI  - A Novel Wind Power Capacity Combined Forecasting method based on Backtracking Search Algorithm
BT  - Proceedings of the 2015 International Industrial Informatics and Computer Engineering Conference
PB  - Atlantis Press
SP  - 720
EP  - 723
SN  - 2352-538X
UR  - https://doi.org/10.2991/iiicec-15.2015.162
DO  - 10.2991/iiicec-15.2015.162
ID  - Yang2015/03
ER  -