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Short-Term Power Load Forecasting of Least Squares Support Vector Machine Based on Wavelet Transform and Drosophila Algorithm
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zhao, Jian-Na He, Xiaobo |
| Copyright Year | 2017 |
| 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. Keywords—Power load forecasting, Wavelet transform, Fruit fly algorithm |
| File Format | PDF HTM / HTML |
| DOI | 10.2991/icemse-17.2017.79 |
| Alternate Webpage(s) | https://download.atlantis-press.com/article/25888234.pdf |
| Alternate Webpage(s) | https://doi.org/10.2991/icemse-17.2017.79 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
