PERFORMANCE OF ARIMA AND NEURAL NETWORK GMDH APPROACHES IN PREDICTION OF NATURAL GAS DEMAND IN VARIOUS SECTORS (IRAN-1380-1389)

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

ABRISHAMI HAMID; JABALAMELI FARKHONDE; ABOLHASANI MASOUME; JAVAN AFSHIN

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF APPLIED ECONOMICS STUDIES IN IRAN Year:2015 | Volume:3 | Issue:12 Page(s): 33-57

Download Full-Text

Persian Verion

View:

1,246

Download:

Cites:

Information Journal Paper

Title

PERFORMANCE OF ARIMA AND NEURAL NETWORK GMDH APPROACHES IN PREDICTION OF NATURAL GAS DEMAND IN VARIOUS SECTORS (IRAN-1380-1389)

Author(s)

ABRISHAMI HAMID | JABALAMELI FARKHONDE | ABOLHASANI MASOUME | JAVAN AFSHIN | Issue Writer Certificate

Keywords

NATURAL GAS DEMAND PREDICTIONQ2

ARIMAQ3

NEURAL NETWORK GMDHQ2

Abstract

Natural gas because of its advantages plays a key role in economy of Iran. Given the continuously rising natural gas consumption, planning in the gas sector by taking into account predicted demand is critical for ensuring sustainable development. In this study we develop models for predicting natural gas demand in residential-commercial, industrial and power plant sectors in Iran using ARIMA (Autoregressive Integrated Moving Average) and NEURAL NETWORK GMDH (Group Method of Data Handling) approaches. The time series data of natural gas demand, natural gas price and air temperature are used as the model variables. the RMSE, MSE and Prediction Error Percentage indexes are used for comparing this models. The prediction accuracy percentage index for residential-commercial, industrial and power plant sectors in ARIMA method are 93.8, 98.3 and 87 percent and those in Network GMDH method are 96.4, 99 and 98.2 percent respectively. The results indicate better performance and accuracy for the GMDH approach compared to the ARIMA model in predicting natural gas demand.

Cites

EXPANDING THE APPLICATION MODELS BOX JENKINS, ARTIFICIAL NEURAL NETWORK AND ADJUSTED EXPONENTIAL FORECASTING SOCIAL PHENOMENA (CASE STUDY: FORECASTING OF MARRIAGE AND DIVORCE IN ILAM)

References

No record.

Cite

APA: Copy

ABRISHAMI, HAMID, JABALAMELI, FARKHONDE, ABOLHASANI, MASOUME, & JAVAN, AFSHIN. (2015). PERFORMANCE OF ARIMA AND NEURAL NETWORK GMDH APPROACHES IN PREDICTION OF NATURAL GAS DEMAND IN VARIOUS SECTORS (IRAN-1380-1389). JOURNAL OF APPLIED ECONOMICS STUDIES IN IRAN, 3(12), 33-57. SID. https://sid.ir/paper/226590/en

Vancouver: Copy

ABRISHAMI HAMID, JABALAMELI FARKHONDE, ABOLHASANI MASOUME, JAVAN AFSHIN. PERFORMANCE OF ARIMA AND NEURAL NETWORK GMDH APPROACHES IN PREDICTION OF NATURAL GAS DEMAND IN VARIOUS SECTORS (IRAN-1380-1389). JOURNAL OF APPLIED ECONOMICS STUDIES IN IRAN[Internet]. 2015;3(12):33-57. Available from: https://sid.ir/paper/226590/en

IEEE: Copy

HAMID ABRISHAMI, FARKHONDE JABALAMELI, MASOUME ABOLHASANI, and AFSHIN JAVAN, “PERFORMANCE OF ARIMA AND NEURAL NETWORK GMDH APPROACHES IN PREDICTION OF NATURAL GAS DEMAND IN VARIOUS SECTORS (IRAN-1380-1389),” JOURNAL OF APPLIED ECONOMICS STUDIES IN IRAN, vol. 3, no. 12, pp. 33–57, 2015, [Online]. Available: https://sid.ir/paper/226590/en

Related Journal Papers