Development of feed-forward network models to predict gas consumption

The development of feedforward artificial neural network based models to predict gas consumption on a daily basis is the subject of this paper. An iterative process based on network sensitivities and intuition to determine the proper input factors is discussed. The methods are applied to gas consump...

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Bibliographic Details
Published inIEEE International Conference on Neural Networks, 1994 Vol. 2; pp. 802 - 805 vol.2
Main Authors Brown, R.H., Kharouf, P., Xin Feng, Piessens, L.P., Nestor, D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1994
Subjects
Artificial neural networks
Companies
Feedforward systems
Heating
Linear regression
Mathematical model
Predictive models
Temperature sensors
Weather forecasting
Wind
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ISBN078031901X
9780780319011
DOI10.1109/ICNN.1994.374281

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Summary:The development of feedforward artificial neural network based models to predict gas consumption on a daily basis is the subject of this paper. An iterative process based on network sensitivities and intuition to determine the proper input factors is discussed. The methods are applied to gas consumption for a region in metropolitan Milwaukee, WI. The obtained results indicate that the feedforward artificial neural network based models reduce the residual predicted consumption root mean squared errors by more than half when compared to models based on linear regression.< >
ISBN:078031901X
9780780319011
DOI:10.1109/ICNN.1994.374281