Short-Term Power Load Forecasting of Least Squares Support Vector Machine Based on Wavelet Transform and Drosophila Algorithm
Jian-Na Zhao, Xiao-Bo He
Available Online December 2017.
- https://doi.org/10.2991/icemse-17.2017.79How to use a DOI?
- Power load forecasting, Wavelet transform, Fruit fly algorithm
- 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.
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 -