Design of artificial neural network models for the prediction of the Hellenic energy consumption

Energy consumption predictions are essential and are required in the studies of capacity expansion, energy supply strategy, capital investment, revenue analysis and market research management. In the recent years artificial neural networks (ANN) have attracted much attention and many interesting ANN...

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Bibliographic Details
Main Authors: Karampelas, P, Vita, V, Pavlatos, C, Mladenov, V, Ekonomou, L
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Computational modeling
Energy consumption
installed capacity
Neurons
prediction
Predictive models
Training
Transfer functions
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Summary:Energy consumption predictions are essential and are required in the studies of capacity expansion, energy supply strategy, capital investment, revenue analysis and market research management. In the recent years artificial neural networks (ANN) have attracted much attention and many interesting ANN applications have been reported in power system areas, due to their computational speed, their ability to handle complex non-linear functions, robustness and great efficiency, even in cases where full information for the studied problem is absent. In this paper, several ANN models were addressed to identify the future energy consumption. Each model has been constructed using different structures, learning algorithms and transfer functions in order the best generalizing ability to be achieved. Actual input and output data were used in the training, validation and testing process. A comparison among the developed neural network models was performed in order the most suitable model to be selected. Finally the selected ANN model has been used for the prediction of the Hellenic energy consumption in the years ahead.
DOI:10.1109/NEUREL.2010.5644049