COMPARISON OF ARTIFICIAL NEURAL NETWORK AND TIME SERIES APPROACH FOR FORECASTING ELECTRICITY CONSUMPTION IN AGRICULTURAL SECTOR

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.

EBRAHIMI M.

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

Journal Paper

Paper Information

Journal: JOURNAL OF AGRICULTURAL ECONOMICS RESEARCH Year:2012 | Volume:4 | Issue:1 (13) Page(s): 27-42

Download Full-Text

Persian Verion

View:

3,537

Download:

Cites:

Information Journal Paper

Title

COMPARISON OF ARTIFICIAL NEURAL NETWORK AND TIME SERIES APPROACH FOR FORECASTING ELECTRICITY CONSUMPTION IN AGRICULTURAL SECTOR

Author(s)

EBRAHIMI M. | Issue Writer Certificate

Keywords

PREDICTIONQ2

ENERGY CONSUMPTIONQ2

ARTIFICIAL NEURAL NETWORKQ2

AGRICULTURAL SECTORQ2

Abstract

The objective of this study was to forecast amount of electricity consumption in AGRICULTURAL SECTOR of Iran. To achieve this objective, time series method of Auto-Regressive Moving Average (ARMA) and ARTIFICIAL NEURAL NETWORKs (ANN) were used. Annual data for period of 1967 to 2008 was used. The Mean Absolute Percent Error (MAPE), Root of Mean of Squared Error (RMSE) and Mean Absolute Error (MAE) criteria were used for comparing the ability of different forecasting methods. Results showed that Feed Forward ARTIFICIAL NEURAL NETWORK with back proportion algorithm could predict electricity consumption with MAPE equal to 1.02% while the corresponding value for time series model was 1.13 percent. Other criteria also revealed the same result, so, ANN is expected to predict electricity consumption more precisely compare to ARMA model.

Cites

References

No record.

Cite

APA: Copy

EBRAHIMI, M.. (2012). COMPARISON OF ARTIFICIAL NEURAL NETWORK AND TIME SERIES APPROACH FOR FORECASTING ELECTRICITY CONSUMPTION IN AGRICULTURAL SECTOR. JOURNAL OF AGRICULTURAL ECONOMICS RESEARCH, 4(1 (13)), 27-42. SID. https://sid.ir/paper/158752/en

Vancouver: Copy

EBRAHIMI M.. COMPARISON OF ARTIFICIAL NEURAL NETWORK AND TIME SERIES APPROACH FOR FORECASTING ELECTRICITY CONSUMPTION IN AGRICULTURAL SECTOR. JOURNAL OF AGRICULTURAL ECONOMICS RESEARCH[Internet]. 2012;4(1 (13)):27-42. Available from: https://sid.ir/paper/158752/en

IEEE: Copy

M. EBRAHIMI, “COMPARISON OF ARTIFICIAL NEURAL NETWORK AND TIME SERIES APPROACH FOR FORECASTING ELECTRICITY CONSUMPTION IN AGRICULTURAL SECTOR,” JOURNAL OF AGRICULTURAL ECONOMICS RESEARCH, vol. 4, no. 1 (13), pp. 27–42, 2012, [Online]. Available: https://sid.ir/paper/158752/en

Related Journal Papers