Multinodal Load Forecasting Using an ART-ARTMAP-Fuzzy Neural Network and PSO Strategy
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Data
2013-01-01
Autores
Antunes, Juliana Fonseca
Souza Araujo, Nelcileno Virgilio de
Minussi, Carlos Roberto [UNESP]
IEEE
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Título de Volume
Editor
Ieee
Resumo
This work presents a system based on Artificial Neural Networks and PSO (Particle Swarm Optimization) strategy, to multinodal load forecasting, i.e., forecasting in several points of the electrical network (substations, feeders, etc.). Short-term load forecasting is an important task to planning and operation of electric power systems. It is necessary precise and reliable techniques to execute the predictions. Therefore, the load forecasting uses the Adaptive Resonance Theory. To improve the precision, the PSO technique is used to choose the best parameters for the Artificial Neural Networks training. Results show that the use of this technique with a little set of training data improves the parameters of the neural network, calculated by the MAPE (mean absolute perceptual error) of the global and multinodal load forecasted.
Descrição
Palavras-chave
Multinodal Load Forecasting, Particle Swarm Optimization, Adaptive Resonance Theory, Artificial Neural Network
Como citar
2013 Ieee Grenoble Powertech (powertech). New York: Ieee, 6 p., 2013.