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Short-Term and Midterm Load Forecasting Using a Bilevel Optimization Model.
- Source :
- IEEE Transactions on Power Systems; May2009, Vol. 24 Issue 2, p1080-1090, 11p, 4 Black and White Photographs, 4 Charts, 3 Graphs
- Publication Year :
- 2009
-
Abstract
- During the last decade, neural networks have emerged as one of the most powerful and accurate nonlinear models for load forecasting. However, using neural networks requires users to have in-depth knowledge to determine the model structure and parameters, which limits their wide application. To overcome this weakness, this paper proposes an integrated approach which combines a self-organizing fuzzy neural network (SOFNN) learning method with a bilevel optimization method. SOFNNs can automatically determine both the model structure and parameters, while the bilevel optimization method automatically selects the best pre-training parameters to ensure that the best fuzzy neural networks be identified. Therefore, the proposed approach is able to automatically identify the best fuzzy neural network for a given forecasting task and is much easier to use in practice. The proposed approach is tested on real-load data from the Southern Power Network of Hebei Province, China, and on the EUNITE competition data. Results show the proposed approach improves existing load forecasting models. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08858950
- Volume :
- 24
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Power Systems
- Publication Type :
- Academic Journal
- Accession number :
- 39366427
- Full Text :
- https://doi.org/10.1109/TPWRS.2009.2016609