Title :
Development of artificial neural network models to predict daily gas consumption
Author :
Brown, Ronald H. ; Matin, Iftekhar
Author_Institution :
Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI, USA
Abstract :
The development of feedforward artificial neural network (ANN) models to predict daily gas consumption is the subject of this paper. A methodology based on network sensitivities and intuition is discussed. The methodology is applied to two regions in Wisconsin served by the Wisconsin Gas Company (WGC). Training results show that ANN models reduce prediction root mean squared errors by more than half when compared with linear regression models. The ANN predictions are compared with predictions made by WGC gas controllers for the first 97 days of the 1994-1995 heating season. The ANN prediction errors are 82.2% and 69.7% of the WGC estimate errors for the two regions
Keywords :
feedforward neural nets; learning (artificial intelligence); natural gas technology; public utilities; Wisconsin Gas Company; artificial neural network models; daily gas consumption prediction; errors reduction; feedforward artificial neural network models; intuition; network sensitivities; prediction root mean squared errors; training; Artificial neural networks; Companies; Feedforward systems; Heating; Linear regression; Mathematical model; Predictive models; Temperature; Weather forecasting; Wind;
Conference_Titel :
Industrial Electronics, Control, and Instrumentation, 1995., Proceedings of the 1995 IEEE IECON 21st International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-3026-9
DOI :
10.1109/IECON.1995.484153
