Title :
Short-term load forecasting using general regression neural network
Author :
Niu, Dong-xiao ; Wang, Hui-Qing ; Gu, Zhi-Hong
Author_Institution :
Sch. of Bus. Adm., North China Electr. Power Univ., Baoding, China
Abstract :
This paper describes an optimal generalized regression neural network (GRNN) in which training of the network is optimization of the smoothing factors. And a modified differential evolution algorithm (MDE) is proposed to improve the searching efficiency of simple differential evolution algorithm (DE). The modified evolution algorithm advanced the performance of global optimizing through collecting population information during evolution and at the same time introducing deterministic operation, amending distribution of individuals adaptively. The eugenic evolution strategies used in this paper include maintaining population diversity, adding new deterministic simplex searching operation, modifying the probability operation, and others. The GRNN-MDE, which is based on DE and provides powerful capacity in non-linear modeling and predicting, is applied to modeling short-term power load forecasting, and the result is satisfied.
Keywords :
evolutionary computation; learning (artificial intelligence); load forecasting; neural nets; power engineering computing; regression analysis; search problems; deterministic operation; deterministic simplex searching; eugenic evolution; modified differential evolution algorithm; network training; nonlinear modeling; nonlinear prediction; optimal generalized regression neural network; population diversity; short-term load forecasting; smoothing factor optimization; Artificial neural networks; Biological neural networks; Electronic mail; Load forecasting; Model driven engineering; Neural networks; Optimization methods; Parameter estimation; Power system modeling; Predictive models; Differential evolution algorithm; eugenic evolution; generalized regression neural network (GRNN); load forecasting;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527651