CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

A Meta Analysis of Natural Gas Consumption

by Prabodh Kumar Pradhan, Sunil Dhal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 22
Year of Publication: 2018
Authors: Prabodh Kumar Pradhan, Sunil Dhal
10.5120/ijca2018916414

Prabodh Kumar Pradhan, Sunil Dhal . A Meta Analysis of Natural Gas Consumption. International Journal of Computer Applications. 179, 22 ( Feb 2018), 19-25. DOI=10.5120/ijca2018916414

@article{ 10.5120/ijca2018916414,
author = { Prabodh Kumar Pradhan, Sunil Dhal },
title = { A Meta Analysis of Natural Gas Consumption },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2018 },
volume = { 179 },
number = { 22 },
month = { Feb },
year = { 2018 },
issn = { 0975-8887 },
pages = { 19-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number22/29000-2018916414/ },
doi = { 10.5120/ijca2018916414 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:56:08.645457+05:30
%A Prabodh Kumar Pradhan
%A Sunil Dhal
%T A Meta Analysis of Natural Gas Consumption
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 22
%P 19-25
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Natural Gas is considered to be one of the leading energy sources for India which provide pollution free, flexible to move to our industry. Due to limited Natural Gas resource, it is a challenge to conserve, utilize our resource in an optimal way. For this a number of authors are tried to predict the natural gas consumption in short and long term basis using mathematical and computational Techniques. The objective of the paper is to meta-analysis the papers published related to Natural Gas consumption for the year 2002-2017. This research helps to find out a better and accurate prediction techniques in short and long term basis for prediction of Natural GasConsumption(NGC).

References
  1. Xiaoyu Wang, Dongkun Luo, Jianye Liu, Wenhuan Wang & Guixin Jie, Prediction of Natural Gas Consumption in different regions of China using a hybrid MVO-NNGBM model, Volume 2017, Article ID 6045708, 10 pages, Mathematical problems in Engineering, Hindawi.
  2. Dario Sebalj, Josip Mesaric & Davor Dujak, Predicting Natural Gas Consumption –A Literature Review, 28th CECIIS, September, 27-29, 2017, Varazdin, Croatia.
  3. Zhenwu Zhang & Xiantao Liu, Prediction of long term gas load based on Particle Swarm optimization and Gray neural network models, Advanced in Mechanical Engineering , 2017 Vol 9(7) 1-8,China.
  4. Beyzanur Cayir Ervural, Omer Faruk Beyca& Selim Zaim, Model estimation of ARMA using genetic algorithms : A case study of forecasting of Natural Gas consumption, 12th International Strategic Management Conference, ISMC 2016, 28-30 October 2016, Antalya, Turkey.
  5. Akpinar , M., & Yumusak, N. (2016). Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods. Energies, 9(9), 727.
  6. Mohsen Hajabdollahi, Mostafa Hosseinzadeh and M.M Ghanadi Arab, Prediction of Long Term Residential Natural Gas Consumption Using ANN, Journal of Applied Mechanical Engineering , Vol-2, Issue-2 , ISSN:4172/2168-9873, 1000120. Iran.
  7. Aras, N. (2008). Forecasting Residential Consumption of Natural Gas Using Genetic Algorithms. Energy Exploration & Exploitation, 26(4), 241-266.
  8. Bai, Y., & Li, C. (2016). Daily natural gas consumption forecasting based on a structure-calibrated support vector regression approach. Energy and Buildings, 127(1), 571-579.
  9. BP Global. (2015). Natural gas consumption Retrieved from http://www.bp.com/en/global/corporate/energy-economics/statistical-review-of -world-energy/natural-gas/natural-gas-consumption.html .
  10. Krzysztof Nęcka, Małgorzata Trojanowska, Short-Term forecasting of Natural Gas demand by Rural Consumers using Regression model, Teka, Commission of Motorization and Energetics in Agriculture-2014, Vol.14, No.4, 93-98. Poland.
  11. Demirel, O., Zaim, S., Caliskan, A., & Ozuyar, P. (2012). Forecasting natural gas consumption in Istanbul using neural networks and multivariate time series methods. Turkish Journal of Electrical Engineering and Computer Sciences, 20(5), 695-711.
  12. Gil, S., & Deferrari, J. (2004). Generalized Model of Prediction of Natural Gas consumption. Journal of Energy Resources Technology, 126(2), 90-98.
  13. oldo, B. (2012). Forecasting natural gas consumption. Applied Energy, 92, 26-37.
  14. Wu, L., Liu, S., Chen, H., & Zhang, N. (2015). Using a Novel Grey System Model to Forecast Natural Gas Consumption in China. Mathematical Problems in Engineering, 2015, 1-7
  15. Zeng, B., & Li, C. (2016). Forecasting the natural gas demand in China using a self-adapting intelligent grey model. Energy, 112(1), 810-825.
  16. Ma, Y., & Li, Y. (2010). Analysis of the supply-demand status of China Natural Gas to 2020. Petroleum Science, 7(1), 132-135.
  17. Zhu, L., Li, M., Wu, Q., & Jiang, L. (2015). Short-term natural gas demand prediction based on support vector regression with false neighbors filtered. Energy, 80, 428-436.
  18. Wu, L., Liu, S., Chen, H., & Zhang, N. (2015). Using a Novel Grey System Model to Forecast Natural Gas Consumption in China. Mathematical Problems in Engineering, 2015, 1-7.
  19. Forouzanfar, M., Doustmohammadi, A., Menhaj, M., & Hasanzadeh, S. (2010). Modeling and estimation of the natural gas consumption for residential and commercial sectors in Iran. Applied Energy, 87(1), 268-274.
  20. Wadud, Z, Dey, HS, Kabir, MA et al, Modeling and forecasting Natural Gas demands in Bangladesh. Energy policy, 39(11). 7372-7380. ISSN 0301-4215.
  21. Omer Fahrettin Dem IREL, Selim Zaim, Ahmet Caliskan & Pinar Ozuyar, Forecasting Natural Gas consumption in Istanbul using Neural Network and Multivariate time series methods. Turkey. Turk J Electrical Engg & Comp. Sc., Vol-20, No-5, 2012.
  22. Ahmet Goncu, Mehmet Oguz Karahan and Tolga Umut Kuzubas , Forecasting Daily Residential Natural Gas Consumption: A Dynamic temperature modeling approach, April 30, 2013.
  23. Potocnik, P.,Thaler, M., Govekar, E., Grabec, I., Oredos, A., 2007 . Forecasting risks of Natural Gas consumption in Slovenia Energy Policy 35, 4271-4282.
  24. Nguyen Hoang Vietand Jacek Mandziuk, Neural and Fuzzy Neural Networks in Prediction of Natural Gas Consumption . Institute of Fundamental Technological Research,. Polish Academy of Sciences,. Swietokrzyska 21 Str., 00-049 Warsaw, Poland.
  25. MSc Almir Beserra dos Santos & DSc Erika Christina Ashton Nunes Chrisman, Natural Gas Consumption Overview In Brazil. Brazil. Quest Journals, Journal of Research in Mechanical Engineering , Volume 3, Issue-3(2017) pp:24-28. ISSN(online):2321-8185.
Index Terms

Computer Science
Information Sciences

Keywords

NGC.

Powered by PhDFocusTM