Use of Artificial Neural Network (ANN) and Time Series Approach for Prediction of Electricity consumption in Agricultural sector

Subject Areas : Agricultural Economics Research

M. Ebrahimi ¹ (عضو هئیت علمی دانشکده اقتصاد و مدیریت دانشگاه آزاد اسلامی واحد شیراز)

Received: 2015-12-17 Accepted : 2015-12-17 Published : 2012-03-20

Keywords: Artificial neural network, Agricultural Sector, energy consumption, Prediction,

Abstract :

The main object of this study is to predict electricity consumption of agricultural sector in Iran. To get the 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. As the result showed Feed Forward artificial neural network with back proportion algorithm can predict electricity consumption with MAPE equal to 1.02%, while the corresponding value for time series model obtained 1.13 percent. Other criteria also revealed the same result, so, ANN is expected to predict electricity consumption more precise than ARMA model. Therefore, energy ministry may use ANN in future predictions.  

References:

1. Amini Fard, A. 2001, Estimate of electricity demand in

residential sector, M.S. thesis. Faculty of Economics and

Management, Shiraz University. (in Persian)

2. Amin Naseri, M. R., and Kochak Zadeh, A. 2006, Study of

ARIMA linear method and fuzzy-artificial neural network non

linear method in prediction of urban gas demand, Journal of

Economic research, , 71: 133-146. (in Persian)

3. Darbelly, G. S. and M. Slama 2000. Forecasting the short-term

demand for electricity, do neural networks stand a better chance?,

International Journal Of Forecasting, 16: 71-83.

4. Esfehaniyan, M. and Amin Naseri, M. R., 2008. Provide an

artificial neural network for short run prediction of petroleum

price, International Journal of Engineering Science, 19: 27-35. (in

Persian)

5. Esmaeili, A., and Tarazkar, M. H. 2005. Estimate of electricity

demand in agricultural sector, Case Study: Fars Province, 5th

Iranian Conference on Agricultural Economics, Iran, Zahedan. (in

Persian)

6. Greene W.H. 2000. Econometric analysis, 3th edition. PrenticeHall,

Englewood Cliffs, New Jersey.

7. Islam, S. M., Al-Alawi, S. M. and K. A. Ellithy 1995. Forecasting

monthly electric load and energy for a fast growing utility using

an artificial neural network, Electric Power Systems Research,

34( 1): 1-9.

8. Kavaklioglu K., Ceylan H., Ozturk H.K., Canyurt, O.E. 2009.

Modeling and prediction of Turkey’s electricity consumption

using Artificial Neural Networks, Energy Conversion and

Management, 50: 2719–2727.

9. Ministry of Energy, 2008. Energy Balance Sheet, Deputy for

Power and Energy Affairs, Power and Energy Planning

Department, Iran. (in Persian)

10. Murat YS, Ceylan H. 2006. Use of artificial neural networks for

transport energy demand modeling. Energy Policy, 34: 3165–72.

11. Pesaran, H.M. and B. pesaran 1997. Working With Microfit 4.0:

An Introduction to Econometrics, Oxford University Press,

Oxford. 

12. Seddighi, H. R, Law ler, K. A. and A. V. Katos 2000.

Econometrics: A Practical Approach, Sunderland Business

School, UK.

13. Shakibai, A. R. and Koochekzadeh, S. 2009. Modeling and

predicting agricultural energy consumption in Iran, AmericanEurasian

J. Agric. & Environ. Sci., 5:308-312.

14. Sourosh, A., Baradaran Kazemzadeh, R. V. and Bahreyni Nejad,

A. 2009. Improvement of regression model by artificial neural

network clustering for prediction of monthly electricity

consumption, Journal of Sharif, 25: 73-83. (in Persian)

15. Sozen A, Arcaklioglu E, Ozkaymak M. 2005. Modelling of the

Turkey’s net energy consumption using artificial neural network.

Int. J. Comput. Appl. Technol., 22(2/3):130–6.

16. Tarazkar, M. H., Najafi, B. 2005. Application of artificial

intelligence in price forecasting Case study: price of rice in Fars

Province, Scientific & Research Quarterly Journal of Bank

Keshavarzi, 9: 181-209. (in Persian)

17. Zibaei, M. and Tarazkar, M. H., 2004. Study of short- and longrun

relationship between energy consumption and value added in

agricultural sector: separately energy component, Scientific &

Research Quarterly Journal of Bank Keshavarzi,6: 157-171. (in

Persian)

18. Zhang, G., Patuwo B. E. and M. Y. Hu 1998, Forecasting With

Artificial Neural Network: The State of Art, International Journal

of Forecasting, 14: 35-62.