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

An integrated approach which combines a self-organizing fuzzy neural network (SOFNN) learning method with a bilevel optimization method that is able to automatically identify the best fuzzy neural networks for a given forecasting task and is much easier to use in practice is proposed.

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. © 2009 IEEE.