Abstract

Electric energy is accounted as one of the major goods in human life, and also have a great role in progressing and developing the several sectors as economics, manufactures and any other sector related to daily use. In this study the monthly demand of electricity in Sulaimani governorate have been used, the main goal of the study is to choose appropriate model to forecast the monthly demand of electric in Sulaimani governorate for 12 months in 2020, the analyzing,results and comparison shows that FSARIMA(0,0,0)x(2,1,0)4 is appropriate model for this mission of forecasting which has minimum AIC among the other candidate models that equal to 0.28

Highlights

  • The methodology of this paperis dependent on Seasonal Autoregressive Integrated Moving Average (SARIMA) model because there is no doupt that the using of electric contain seasonal effect, and the four season in our country are distingushed

  • The processes and expansion of modern organizations are completely need time series data public and personal foundation are using time series data to manage the networks, more use this type of data to understanding of thousands of time series data that consist of economic and financial information so in any field of life time series are necessary

  • The reality about data information dots that accepted through the time, use time series analysis and it must be acceptable inside template like(AR,trend or seasonal variation).as previously defined time series analysis acclimated expectation in statistics and other quality of data so that is mean use time series to drawing suitableinference, so the main purpose of time series analysis is forecasting

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Summary

Introduction

The methodology of this paperis dependent on Seasonal Autoregressive Integrated Moving Average (SARIMA) model because there is no doupt that the using of electric contain seasonal effect, and the four season in our country are distingushed. The form of SARIMA model is (p,d,q)x(P,D,Q,s)where the first part (p,d,q) is non-seasonal ARIMA model and the second part (P,D,Q,s) is the seasonal. The seasonal part of the model consists of terms that are similar to the non-seasonal components of the model, but involve backshifts of the seasonal period. The application section contain the data that was used in this paper that comes from directory of electricity in Sulaimani city for monthly demand, whilefitted six different models which are presented, and the maximum likelihood estimation method have been used to estimate the parameter of the best model that is adequate the data under consideration The application section contain the data that was used in this paper that comes from directory of electricity in Sulaimani city for monthly demand, whilefitted six different models which are presented in table 3.3, and the maximum likelihood estimation method have been used to estimate the parameter of the best model that is adequate the data under consideration

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