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Research of least squares support vector regression based on differential evolution algorithm in short-term load forecasting model.
- Source :
- Journal of Renewable & Sustainable Energy; 2014, Vol. 6 Issue 5, p1-10, 10p, 2 Diagrams, 2 Charts, 4 Graphs
- Publication Year :
- 2014
-
Abstract
- To improve the accuracy of short-term load forecasting, a differential evolution algorithm (DE) based least squares support vector regression (LSSVR) method is proposed in this paper. Through optimizing the regularization parameter and kernel parameter of the LSSVR by DE, a short-term load forecasting model which can take load affected factors such as meteorology, weather, and date types into account is built. The proposed LSSVR method is proved by implementing short-term load forecasting on the real historical data of Yangquan power system in China. The average forecasting error is less than 1.6%, which shows better accuracy and stability than the traditional LSSVR and Support vector regression. The result of implementation of short-term load forecasting demonstrates that the hybrid model can be used in the short-term forecasting of the power system more efficiently [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19417012
- Volume :
- 6
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- Journal of Renewable & Sustainable Energy
- Publication Type :
- Academic Journal
- Accession number :
- 99221943
- Full Text :
- https://doi.org/10.1063/1.4900552