Details

Autor(en) / Beteiligte

• Huina Mao
• Xiao-Jun Zeng
• Gang Leng
• Yong-Jie Zhai
• Keane, J.A.

Titel

Short-Term and Midterm Load Forecasting Using a Bilevel Optimization Model

Ist Teil von

• IEEE transactions on power systems, 2009-05, Vol.24 (2), p.1080-1090

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2009

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• 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.