A fast electric load forecasting using adaptive neural networks

• Autor(a) principal: Lopes, M. L M [UNESP]
• Data de Publicação: 2003
• Outros Autores: Lotufo, A. D P [UNESP], Minussi, C. R. [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/PTC.2003.1304158; http://hdl.handle.net/11449/67494
• topic: Adaptive parameters; Backpropagation algorithm; Electrical load forecasting; Fuzzy controller; Fuzzy logic; Neural networks; Postsynaptic function; Adaptive neural networks; Adaptive process; Faster convergence; Fuzzy controllers; Global errors; Multilayer feedforward neural networks; Network training; Two parameter; Backpropagation algorithms; Electric loads; Feedforward neural networks; Network architecture; Electric load forecasting

This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.