Load forecasting in the current, increasingly liberalized, electricity power market is of crucial importance as a means for producers to optimize and rationalize energy supply. A number of electric power companies are equipped to make forecasts with the aid of traditional statistical methods. This paper presents the use of an artiﬁcial neural net to an hourly based load forecasting application for a small electric grid on Italian island (Lipari) not connected to the mainland. The aim was to examine the forecasting ability of neural net in a situation where the electric load was subject to considerable seasonal variations over the year. The variations are aﬀected by energy demand related to the tourism season as well as by climatic conditions, especially temperature. The network developed was a multi-layer perceptron type built on three layers trained with a back-propagation algorithm. The input layer receives all the most relevant information regarding: the class of day type, the hour in the daytime, the load and background temperature recorded at the indicated time for the months of March, August and October whilst the output layer provides the forecast value at the indicated time in December. The results obtained are encouraging; in the training phase the RMS error rate was around 2% and this rate settled at about 2.6% during testing. As both the margins of error recorded are acceptable, the use of a neural network for electric loadforecasting applications can be considered an attractive option.
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