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Neural Network based Forecasting Model for Natural Gas Consumption

Prabodh Kumar Pradhan, Sunil Dhal, Nilayam Kumar Kamila. Published in Artificial Intelligence.

Communications on Applied Electronics
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Prabodh Kumar Pradhan, Sunil Dhal, Nilayam Kumar Kamila
10.5120/cae2017652727

Prabodh Kumar Pradhan, Sunil Dhal and Nilayam Kumar Kamila. Neural Network based Forecasting Model for Natural Gas Consumption. Communications on Applied Electronics 7(11):1-8, December 2017. BibTeX

@article{10.5120/cae2017652727,
	author = {Prabodh Kumar Pradhan and Sunil Dhal and Nilayam Kumar Kamila},
	title = {Neural Network based Forecasting Model for Natural Gas Consumption},
	journal = {Communications on Applied Electronics},
	issue_date = {December 2017},
	volume = {7},
	number = {11},
	month = {Dec},
	year = {2017},
	issn = {2394-4714},
	pages = {1-8},
	numpages = {8},
	url = {http://www.caeaccess.org/archives/volume7/number11/787-2017652727},
	doi = {10.5120/cae2017652727},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Natural resource is a limited resource and its utilization with proper planning is utmost important for human being. Natural gas is one of the highly consumed natural resource which requires a proper planning and utilization. Environmental factor such as temperature, humidity is few of the factors which effect the natural gas consumption. In this paper, the consumption of natural gas is evaluated with respect to environmental factors through neural network based approach and compared the forecasting results. The analysis results and comparison model show the better results and could be used in forecasting of the future natural gas consumption.

References

  1. Pradhan, P.K., Nayak, B., Dhal S.K., 2016. Time Series Data Prediction of Natural Gas Consumption Using Arima Model. International Journal of Information Technology & Management Information System, 7(3). 1–7.
  2. Hongwei, M., Yonghe W., 2009. Grey Predictive on Natural Gas Consumption and Production in China. Web Mining and Web-based Application.
  3. Pradhan, P.K., Dhal, S.K.,2015. A Survey of Different Prediction Models & Role of Artificial Neural Networks for Natural Gas Consumption. International Journal of Science and Research (IJSR), 4(11).1325-1328
  4. https://www.eia.gov/energyexplained/index.cfm
  5. XGilardoni, A., Demand for Natural Gas: Trends and Drivers in The World Market for Natural Gas Springer Berlin Heidelberg. 39-60
  6. http://www.weatherbase.com/weather/countryall.php3
  7. Brown, R.H., Vitullo, S.R., Corliss, G.F.,2015. Detrending daily natural gas consumption series to improve short-term forecasts in Power & Energy. IEEE Society General Meeting.
  8. https://www.numbeo.com/cost-of-living/country_price_rankings?itemId=105
  9. https://www.studentenergy.org/topics/natural-gas
  10. Licheng, S., Ronghua, H., 2009. Study on the relationship between the energy consumption and economic system of Jiangsu Province base on grey relational analysis. IEEE Grey Systems and Intelligent Services,
  11. https://en.wikipedia.org/wiki/List_of_countries_by_natural_gas_consumption
  12. http://ahnutritiontherapy.com/338/foods-to-fight-humidity
  13. https://www.epa.gov/climate-impacts/climate-impacts-agriculture-and-food-supply.
  14. https://www.wunderground.com/history/airport/VEBS/2012/1/1/MonthlyHistory.html

Keywords

Natural Gas, Natural Gas Consumption, Consumption factors