Short-Term Power Load Forecasting of Least Squares Support Vector Machine Based on Wavelet Transform and Drosophila Algorithm

DOI

10.2991/icemse-17.2017.79How to use a DOI?

Keywords

Power load forecasting, Wavelet transform, Fruit fly algorithm

Abstract

As an energy that can't be stored and related to the national economy and the people's livelihood, the stability of electric energy has been paid more and more attention in our country. In order to solve this problem, a short-term least squares support vector machine (SVM) based on wavelet decomposition and Drosophila algorithm is proposed to predict short-term power load. The example shows that WT-FOA-LSSVM has been improved obviously in the prediction precision, and has certain applicability.

Copyright

© 2017, 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  - Jian-Na Zhao
AU  - Xiao-Bo He
PY  - 2017/12
DA  - 2017/12
TI  - Short-Term Power Load Forecasting of Least Squares Support Vector Machine Based on Wavelet Transform and Drosophila Algorithm
BT  - Proceedings of the 2017 2nd International Conference on Education, Management Science and Economics (ICEMSE 2017)
PB  - Atlantis Press
SP  - 322
EP  - 325
SN  - 2352-5428
UR  - https://doi.org/10.2991/icemse-17.2017.79
DO  - 10.2991/icemse-17.2017.79
ID  - Zhao2017/12
ER  -