Abstract
In this paper an accurate neural network model is proposed for the gas consumption in Belgium. It is a static non-linear model, based on monthly data and contains the following inputs: temperature, difference between real and expected temperature, oil price, number of domestic clients and consumption by industry. Various interpretations are made on the identified models such as yearly error, normalized gas consumption, growth rate, uncertain linear model interpretation and sensitivity of the consumption with respect to the temperature. In contrast to traditional models, which depend only on the temperature, the present neural network models show excellent generalization ability, with small yearly errors on training and test set.
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This research work was carried out at the ESAT laboratory and the Interdisciplinary Center of Neural Networks, ICNN, of the Katholieke Universiteit Leuven in the framework of the Belgian Programme on Inter-university Poles of Attraction, initiated by the Belgian State, Prime Minister's Office for Science, Technology and Culture (IUAP-17), and in the framework of a Concerted Action Project MIPS (Model-based Information Processing Systems) of the Flemish Community, It is sponsored by Electrabel.
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Suykens, J., Lemmerling, P., Favoreel, W. et al. Modelling the Belgian gas consumption using neural networks. Neural Process Lett 4, 157–166 (1996). https://doi.org/10.1007/BF00426024
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DOI: https://doi.org/10.1007/BF00426024