Antunes, Juliana FonsecaSouza Araujo, Nelcileno Virgilio deMinussi, Carlos Roberto [UNESP]IEEE2020-12-102020-12-102013-01-012013 Ieee Grenoble Powertech (powertech). New York: Ieee, 6 p., 2013.http://hdl.handle.net/11449/196091This 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.6engMultinodal Load ForecastingParticle Swarm OptimizationAdaptive Resonance TheoryArtificial Neural NetworkTrabalho apresentado em eventoWOS:000387091900294