Research on Gas Load Forecasting Using Artificial Neural Network

Article Preview

Abstract:

In this paper, we give a short survey and analysis on natural gas load forecasting technology using artificial neural network. Different input variables are used to compare the result of forecasting the short term gas load. The experiment results show that the BP neural network can be used to find the implicit relation among historical gas load, weather condition and the future gas load. We also conclude that the input variables have no important influence on the accurate forecast.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

423-427

Citation:

Online since:

July 2013

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Changhao Xia, Jian Wang, Karen McMenemy. Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks, Electrical Power and Energy Systems 32 (2010) 743–750

DOI: 10.1016/j.ijepes.2010.01.009

Google Scholar

[2] GORUCU F B , GUMRAH F. Evaluation and forecasting of gas consumption by statistical analysis. Energy Sources, 26 (3),2004,267-276.

DOI: 10.1080/00908310490256617

Google Scholar

[3] VONDRACEKJ, PEL IKAN E and KONAR O. A statistical model for the estimation of natural gas consumption. Applied Energy, 85 (5) ,2008,pp.362-370

Google Scholar

[4] Shahwan, T., Odening, M., Computational Intelligence in Economics and Finance, Springer, Berlin Heidelberg, New York, p.63–74, 2007.

Google Scholar

[5] YANG Zhao, LIU Yan, MIAO Zhibin and LIU Zhenneng, Application of Neural network in Natural Gas Load Forecasting, Gas & Heat, 23(6),p.331—332, 2003.

Google Scholar

[6] Dillon TS, Morsztyn K, Phua K. Short term load forecasting using adaptive pattern recognition and self-organizing techniques. In: Proceedings of the fifth world power system computation conference (PSCC-5), September 1975, Cambridge, paper 2.4/3, p.1–15.

Google Scholar

[7] Kermanshahi B, Iwamiya H. Up to year 2020 load forecasting using neural nets. Int J Electr Power Energy Syst 2002;24:789–97

DOI: 10.1016/s0142-0615(01)00086-2

Google Scholar

[8] Ghiassi M, Zimbra DK, Saidane H. Medium term system load forecasting with a dynamic artificial neural network model. Electr Power Syst Res 2006;76:302–16.

DOI: 10.1016/j.epsr.2005.06.010

Google Scholar

[9] Carpinteiro OAS, Lemeb RC, Souza ACZ, Pinheiro CAM, Moreira EM. Long-term load forecasting via a hierarchical neural model with time integrators. Electr Power Syst Res 2007;77:371

DOI: 10.1016/j.epsr.2006.03.014

Google Scholar

[10] Swales, G. S., & Yoon, Y. Applying artificial neural networks to investment analysis. Financial Analysts Journal, 48 (1992).78–80.

DOI: 10.2469/faj.v48.n5.78

Google Scholar