Development of feed-forward network models to predict gas consumption
Ronald H. Brown, P. Kharouf, Xin Feng, L.P. Piessens, D. Nestor · 2002
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.>