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Application of improved artificial neural networks in short-term power load forecasting.
- Source :
-
Journal of Renewable & Sustainable Energy . Jul2015, Vol. 7 Issue 4, p1-12. 12p. 4 Diagrams, 3 Charts, 3 Graphs. - Publication Year :
- 2015
-
Abstract
- Power load forecasting is a key element for power system management and planning. However, it has been proven to be a hard task due to various unstable factors. This paper presents a forecasting methodology based on this particular type of neural network. The scope of this study presents a solution for short-term load forecasting based on a three-stage model which starts with pattern recognition via self-organizing map (SOM), a clustering of the previous partition by K-means algorithm, and finally demand forecasting for each cluster with back propagation neural network (BPNN) improved by additional momentum and variable learning rate methods. The effectiveness of SOM-K-BPNN model has been verified by the final simulation which shows that the proposed model outperforms the BPNN model with default parameters and Grey System GM (1, 1); therefore, empirical results show that the proposed SOM-K-BPNN model is feasible and can fulfil the short-term load forecasting requirements of China. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19417012
- Volume :
- 7
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Journal of Renewable & Sustainable Energy
- Publication Type :
- Academic Journal
- Accession number :
- 109288798
- Full Text :
- https://doi.org/10.1063/1.4926771