Short-term Load Forecasting Based on FCM and Complex Gaussian Wavelet SVM

DOI

10.2991/iske.2007.25How to use a DOI?

Keywords

complex Gaussian wavelet support vector machine; fuzzy c-means clustering; short-term load forecasting; phase space reconstruction; complex Gaussian wavelet kernel; support vector machine.

Abstract

Complex Gaussian wavelet support vector machine (CGW-SVM) is constructed with complex Gaussian wavelet kernel function for short-term load forecasting (STLF). Based on the chaotic characteristics of short-term load time series, the series is reconstructed with phase space reconstruction theory (PSRT). Then the vector of phase space is used as the input of CGW-SVM. Considering the periodical feature of power loads, the fuzzy c-means (FCM) clustering algorithm is introduced to reduce sample data. The experiments conducted in this paper show that, with the proposed method, the accuracy of load forecasting results is improved and the forecasting process is speeded up.

Copyright

© 2007, 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  - Yongkang Zheng
AU  - Weirong Chen
AU  - Chaohua Dai
AU  - Shengyong Ye
PY  - 2007/10
DA  - 2007/10
TI  - Short-term Load Forecasting Based on FCM and Complex Gaussian Wavelet SVM
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 144
EP  - 147
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.25
DO  - 10.2991/iske.2007.25
ID  - Zheng2007/10
ER  -