Future estimation for Electricity interruption in Dohuk govern orate (Kurdistan-Iraq) using SARIMA model

Main Article Content

Nabeel George Nacy
Mohammed AbdulMajeed Badal
Samaher Tareq Ibrahim


electricity interruption, Time series, SARIMA model, MAE, BIC, RMSE, MAPE, Box-Ljung statistics


 Seasonality, in time series, refers to a regular pattern of changes that repeat for S time period, where S defines the numbers of timer periods until the pattern repeats again. Seasonality, of course, usually causes the time series to be non stationary [4], seasonal differencing is defined as a difference between a value and a value with lag that is a multiple of S.

     In this paper, we will discuss the daily average of hours of electricity interruption per month

which represents the gap between the amount of energy available and energy required for consumption. We took the data of Dohuk governorate (within Kurdistan region of Iraq) for the period from Jan. 2010 to Dec. 2016. Since this series have seasonal affects, we used SARIMA model with S = 12 and the seasonal and non-seasonal differences are 0 or 1, to choose the appropriate model, that gives least value of BIC, RMSE, MAE, MAPE and largest value of R2 to forecast the periods of daily interruption for the next months.

    We concluded that the best model is SARIMA(1,1,1)(0,1,0)12, and the expected values of the daily average of hours of electricity interruption per month in Dohuk Governorate are increasing and it is expected that the entirely lack of electricity supply during the month of December 2018 if the time series continues this pattern.

Key words

Abstract 41 | PDF Downloads 35


1.Adhistya, E.P.,Indriana,H.,Isna, A.(2013),"SARIMA(Seasonal ARIMA) Implementation on time series to forecast the number of Malaria incidence", Information Technology and Electrical Engineering ,conference on Yogyakarta, Indonesia .
2. Akapanta,A.C.,Okorie, I.E., Okoye, N.N.(2015)"SARIMA Modeling of frequency of Monthly Rainfall in Umuahia, Abia State of Nigeria, American Journal of Mathematics and Statistics , 5(2):82-87.
3. Box, G. E. P., Jenkins, G. M.(1970),"Time Series Analysis Forecasting and Control, Holden-Day, San Francisco ,CA.
4. Box,G.E. P., Jenkins,G.M., and Reinsel, G. C. (1994).” Time Series Analysis: Forecasting and Control”, 3rd ed. Prentice Hall, Upper Saddle River, N.J.
5. Brockwell, P., Davis, R.(2002),"Introduction to time series and forecasting ",New york: springer .
6. Chan, N. H. (2002),” Time Series Applications to Finance”, John Wiley & sons,INC., publication, USA; ISBN 0-471-41117-5.
7. Chu,F.(2009),"Forecasting tourism demand with ARIMA-based methods" ,Tourism Management,Vol32,No.5,pp740-751 .
8. Franses, Ph. H. and Dijk, D.V. (2000), “ Nonlinear Time Series Models in Empirical Finance ” Cambridge University Press, ISBN 0 511 01100 8 virtual.
9. Gikungu,S.W.,Waititu, A.G. (2015),"Forecasting Inflation Rate in Kenya Using SARIMA Model American Journal of The oretical and Applied Statistics 4,15-18 . 10. Luo, C.S.,Zhou,L.,Qingfeng, W.(2013)," Application of SARIMA Model in Cucumber Price Forecast", Applied Mechanics and Materials,Vols.373-375,pp.1686-1690 .
11. Mira,S.K., Ahmad M.R.(2015),"Time Series Models for Average monthly Solar radiation in Malaysia" ,Research and Education in Mathematics, International Conference Kuala Lumpur ,Malaysia .