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

Document Type : Research Note


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

2 Petroleum University of Technology, Ahwaz,, I.R. IRAN


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.


Main Subjects

[1] Bouman M.J., Human Diagnostic Reasoning by Computer: An Illustration from Financial Analysis, Mgmt Sci., 29, p. 653 (1983).
[2] Kandil N., Wamkeue R., Saad M., Georges S., An Efficient Approach for Short Term Load Fforecasting Using Artificial Neural Networks, Electrical Power and Energy Systems, 28, p. 525 (2006).
[3] Box G.E., Jenkins G.M., "Time Series Analysis Forecasting and Control",San Francisco, Holden-Day, (1976).
[4] Vemuri S., Huang W.L., Nelson D.J., On Line Algorithms for Forecasting Hourly Loads of an Electric Utility, IEEE Trans. On Power Aapparatus and Systems, PAS-100, p. 3775 (1981).
[5] Rahman S., Bhatnagar S., An Expert System Based Aalgorithm for Short-Term Load Forecasting; IEEE Trans. On Power Systems, 3, p. 392 (1998).
[6] Lee K.Y., Cha Y.T., Park, J.H. "Artificial Neural Network Methodology for Short-Term Load Forecasting", NSF Workshop on ANN Methodology in Power System Engineering, Clenson University, SC, 9-10 Apr., (1990).
[7] Bakirtzis A.G., Petridis V., Klartzis S.J., Alexiadis M.C., Malssis A.H., A Neural Network Short Term Load Forecasting Model for the Greek Power System, IEEE Trans. on Power System, 11(2), p.858 (1996).
[8] Khotanzad A., Hwang R.C., Abaye A., Maratukulam D., An Adaptive Modular Artificial Neural Network Hourly Utilities, IEEE Trans. on Power Systems, 10(3), p. 1716 (1995).
[9] Khotanzad A., Afkhami-Rohani R., Lu T.L., Davis M.H., Abaye A., Maratukulam D., ANNSTLF, A Neural Network Based Electric Load Forecasting System, IEEE Trans. On Neural Networks, 8(4), p. 835 (1997).
[10] Khotanzad A, Afkhami-Rohani R., Maratukulam D., ANNSTLF, Artificial Neural Network Based Short Term Load Forecaster-Generation Three; IEEE Trans. on Power Systems, 13(4), p. 1413. (1998).
[11] Srinivasan D., Tan S.S., Chang C.S., Chan E.K., Parallel Neural Network Fuzzy Expert System Strategies for Short Term Load Forecasting: System Implementation and Performance Evaluation, IEEE Trans. on Power Systems, 14(3), p. 1100 (1999).
[12] Zhang G.P., An Investigation of Neural Network for Linear Time Series Forecasting, Computers and Operation Research, 28, p. 1183 (2001).
[13] Zhang G, Patuwo BE, Hu MY.,Forecasting with Artificial Neural Networks:The State of the Art., International Journal of Forecasting, 14, p. 35 (1998).
[14] Manohar H.J., Saravanan R., Renganarayanan S., Modelling of Steam Fired Double Effect Vapour Absorption Chiller Using Neural Network, Energy Conversion and Management, 47, p. 2202 (2006).
[15] Adel Malallah, Ibrahim Sami Nashawi, Estimating the Fracture Gradient Coefficient Using Neural Networks for a Field in the Middle East, Journal of Petroleum Science and Engineering, 49, p. 193 (2005).
[16] Mousumi Chakraborty, Chiranjib Bhattacharya, Siddhartha Dutta, Studies on the Applicability of Artificial Neural Network (ANN) in Emulsion Liquid Membranes, Journal of Membrane Science, 220, p. 155 (2003).
[17] Beale R., Jackson T., "Neural Computing: An Introduction", Institude of Physics Publishing, (1998).
[18] Gorr L., Research Prospective on Neural Network Forecasting, International Journal of Forecasting, 10, p. 1 (1993).
[19] Satish B., Swarup K.S., Srinivas S., Hanumantha Rao A., Effect of Temperature on Short Term Load Forecasting Using an Integrated ANN, Electric Power Systems Research, 72, p. 95 (2004).
[20] Adel Malallah, Ibrahim Sami Nashawi, Estimating the Fracture Gradient Coefficient Using Neural Networks for a Field in the Middle East, Journal of Petroleum Science and Engineering, 49, p. 193 (2005).
[21] Hosoz M., Ertunc H.M., Bulgurcu H., Performance Prediction of a Cooling Tower Using Artificial Neural Network, Energy Conversion and Management, 48, p. 1349 (2007).
[22] Islamoglu Y., Kurt A., Heat Transfer Analysis Using ANNs with Experimental Data for Air Flowing in Corrugated Channels, International Journal of Heat and Mass Transfer, 47, p. 1361 (2004).
[23] P. Lamb, W.G.P., D. Logue, E.S.I. , "Implimentation of a Gas Load Forecaster At Williams Gas Pipeline, Pipeline Simulation Interest Group, (2001).
[24] Dr John Piggott, Advantica Ltd,"Accurate Load Forecasting, You Can not be Serious", Pipeline Simulation Interest Group, (2003).
[25] Ashouri F., An Expert System for Predicting Gas Demand: A Case Study, OMEGA, Int., J. of Mgmt Sci., 21(3), p. 307 (1993).