Electric energy demand forecasting with neural networks

Abstract

Electric energy demand forecasting represents a fundamental information to plan the activities of the companies that generate and distribute it. So a good prediction of its demand will provide an invaluable tool to plan the production and purchase policies of both generation and distribution or reseller companies. This demand may be seen as a temporal series when its data are conveniently arranged. In this way the prediction of a future value may be performed studying the past ones. Neural networks have proved to be a very powerful tool to do this. They are mathematical structures that mimic that of the nervous system of living beings and are used extensively for system identification and prediction of their future evolution. In this work a neural network is presented to predict the evolution of the monthly demand of electric consumption. A feedforward multilayer perceptron (MLP) has been used as neural model with backpropagation as learning strategy. The network has three hidden layers with a 8-4-8 distribution. It takes twelve past values to predict the following one. Errors smaller than 5% have been obtained in most of the predictions.