Azari, A., Shariaty-Niassar, M., Alborzi, M. (2012). Short-term and Medium-term Gas Demand Load Forecasting by Neural Networks. Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 31(4), 77-84.

Ahmad Azari; Mojtaba Shariaty-Niassar; Mahmoud Alborzi. "Short-term and Medium-term Gas Demand Load Forecasting by Neural Networks". Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 31, 4, 2012, 77-84.

Azari, A., Shariaty-Niassar, M., Alborzi, M. (2012). 'Short-term and Medium-term Gas Demand Load Forecasting by Neural Networks', Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 31(4), pp. 77-84.

Azari, A., Shariaty-Niassar, M., Alborzi, M. Short-term and Medium-term Gas Demand Load Forecasting by Neural Networks. Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 2012; 31(4): 77-84.

Short-term and Medium-term Gas Demand Load Forecasting by Neural Networks

^{1}School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, I.R. IRAN

^{2}Petroleum University of Technology, Ahwaz,, I.R. IRAN

Abstract

The ability of Artificial Neural Network (ANN) for estimating the natural gas demand load for the next day and month of the populated cities has shown to be a real concern. As the most applicable network, the ANN with multi-layer back propagation perceptrons is used to approximate functions. Throughout the current work, the daily effective temperature is determined, and then the weather data with the gas consumption data of the last days are used for network training. It is shown that nearly 93% and 98.9% of the result is in a good agreement with the real data for the daily gas load forecasting and those of the monthly respectively. These results clearly show the capability of the presented networks. The method, however, can further be developed for prediction of other required information in various industries.

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