Volume 9 Issue 1 Article 11

Forecasting Electricity Generation in Kurdistan Region Using BOX-Jenkins Model

Authors: Wasfi T. Saalih Kahwachi1 & Samyia Khalid Hasan2
1Director of Research Center, Tishk International University, Erbil, Iraq
2Statistics and Informatics Department, Salahaddin University-Admin.& Economics, Erbil, Iraq

Abstract: The objective of this research is to identify the best and most relevant statistical model for projecting electrical power generation in the KRI. Data was collected for this purpose throughout a 168-year period (2006-2019). The Box-Jenkins technique was used, and it was discovered that the series is unstable and not random after analyzing it. The essential transformations, namely the square root and the first difference, were used to achieve stability and randomization. The necessary transformations, such as the square root and the first difference, were used to achieve stability and randomness. the analysis showed that ARIMA (2,1,2) is the most appropriate model among the proposed models using some statistical criteria like (AIC, BIC, MSE, MAPE, and RMSE) were used to obtain the model that can be utilized in the prediction. A simulation was conducted in favor to the selected model.

Keywords: Electricity Generation, Time Series, Box-Jenkins, Forecasting, Simulation

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Doi: 10.23918/eajse.v9i1p142

Published: January 16, 2023

References

Adler, R.J. (1990). “An Introduction to Continuity, Extra-ma, and Related Topic for General Gaussian Processes”, Lecture Notes- Monograph Series 12, Institute of Mathematical Statistics, Hayward. CA.

Atiya, A. F.; El-Shoura, S. M. Shaheen, S. I. and El-Sherif, M. S. (1999). “A Comparison between Neural-Network Forecasting Techniques—Case Study: River Flow Forecasting”, IEEE Transactions on Neural Networks, 10(2), March.

Brown, R.G. (1963). “Smoothing Forecasting and Prediction of Disc-rete Time Series”. Englewood Cliffs, Nj: Prentice-Hall.

Box, G.P. and Jenkins, G.M. (1976). “Time Series Analysis Forecasting and Control”, Revised Edition Holden-Day Inc. San Fran- cisco.

Chatfield, C. (1980). “The Analysis of Time Series: An Introduction”, Bath University, 2nd ed., UK.

Gershenson, Carlos. (1998). “Artificial Neural Networks for Beginners”, Sussex Academy, UK.

Handcock, M.S.and Stein, M.L. (1993).”A Bayesian Analysis of Kriging”. Technimetrics, 35(4).

Hamilton, J. D. (1994). “Time Series Analysis”, Princeton University Press, New Jersey.

Lin, Feng; Yu, Xing Huo; Gregor, Shirely and Irons, Richard. (1995). “Time Series Forecasting with Neural Networks”, Complexity International, 02, ISSN 1320-0682, Australia.