Proceedings of the 2017 2nd International Conference on Education, Management Science and Economics (ICEMSE 2017)

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

Authors
Jian-Na Zhao, Xiao-Bo He
Corresponding Author
Jian-Na Zhao
Available Online December 2017.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

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  - https://doi.org/10.2991/icemse-17.2017.79
ID  - Zhao2017/12
ER  -