Title :
Development of feed-forward network models to predict gas consumption
Author :
Brown, Ronald H. ; Kharouf, Paul ; Feng, Xin ; Piessens, Luc P. ; Nestor, Dick
Author_Institution :
Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
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
Keywords :
feedforward neural nets; iterative methods; public utilities; USA; Wisconsin; daily gas consumption prediction; feedforward neural network based models; intuition; iterative process; metropolitan Milwaukee; network sensitivities; residual predicted consumption root mean squared errors; Artificial neural networks; Companies; Feedforward systems; Heating; Linear regression; Mathematical model; Predictive models; Temperature sensors; Weather forecasting; Wind;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374281
