Predicting Electricity Consumption in Misan Province of Iraq Using Univariate Time Series Analysis

  • Sami A. S. AL-Farttoosi
  • Behzad Mansouri
Palabras clave: Time Series Forecasting, Electricity Consumption, ETS Model, Box-Jenkins Models, State-Space Models

Resumen

The goal of this research is to develop a suitable model for forecasting monthly electricity demand in Misan Iraq. Regarding the issue of electricity shortages and post-war industries rebuilding, this information is vital for Iraqi officials. Due to the lack of information on other variables those affecting power con- sumption, the focus of this research is on univariate models for short-term forecasting (up to two years). The data for this study are from January 2009 to June 2019. Several models were fitted to the data in three classes including exponential smoothing methods, Box-Jenkins models and state- space mod- els. Different criteria were used to select the appropriate model. The random- ness of the model residuals was investigated using Liang-Box criterion and the Akaike information benchmarks were calculated for each model. Also, a 12-month period was excluded from the latest data as the hold-out sample and used to test and validate the models predictions. The results show that Box-Jenkins modeling provides better results for these data. Finally, electric- ity consumption forecasts for a 24-month period in Iraq’s Misan province are presented.

Biografía del autor/a

Sami A. S. AL-Farttoosi
Misan University, Misan, Iraq
Behzad Mansouri
Shahid Chamran University of Ahvaz, Ahvaz, Iran

Citas

Abdel-Aal, RE, and AZ Al-Garni

Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis. Energy 22(11):1059-1069.

Box, George EP, et al.

Time series analysis: forecasting and control: John Wiley & Sons.

Brown, Robert Goodell 1959 Statistical forecasting for inventory control: McGraw/ Hill.

Chatfield, Chris

Model uncertainty and forecast accuracy. Journal of

Forecasting 15(7):495-508. Cho, Haeran, et al.

Modeling and forecasting daily electricity load curves: a hybrid approach. Journal of the American Statistical Association 108(501):7-21.

Durbin, James, and Siem Jan Koopman

Time series analysis by state space methods: Oxford university press.

Ediger, Volkan Ş, and Sertac Akar

ARIMA forecasting of primary energy demand by

fuel in Turkey. Energy policy 35(3):1701-1708. Fildes, RA

Beyond forecasting competitions. International Jour- nal of Forecasting 17(4):556-560.

Fildes, Robert, et al.

Forecasting and operational research: a review. Jour- nal of the Operational Research Society 59(9):1150-1172.

Gardner, Everette S, and Eddie McKenzie

Why the damped trend works. Journal of the Opera- tional Research Society 62(6):1177-1180.

Gardner Jr, Everette S

Exponential smoothing: The state of the art—Part II. International journal of forecasting 22(4):637-666.

Gardner Jr, Everette S, and ED McKenzie

Forecasting trends in time series. Management Sci- ence 31(10):1237-1246.

Harrison, PJ

Exponential smoothing and short-term sales fore-

casting. Management Science 13(11):821-842. Harvey, Andrew C

A unified view of statistical forecasting procedures. Journal of forecasting 3(3):245-275.

Helske, Jouni, and Maintainer Jouni Helske 2019 Package ‘KFAS’.

Holt, Charles C

Forecasting Seasonals and Trends by Exponentially

Weighted Moving Averages. CARNEGIE INST OF TECH PITTS- BURGH PA GRADUATE SCHOOL OF INDUSTRIAL ....

Huss, William R

a Comparative analysis of company forecasts and ad- vanced time series techniques using annual electric utility energy sales data. International Journal of Forecasting 1(3):217-239.

b The teachers/practitioners corner comparative anal- ysis of load forecasting techniques at a southern utility. Journal of Forecasting 4(1):99-107.

Hyndman, R, et al.

Forecast: forecasting functions for time series and linear models. R package version 8.5. 2019.

Hyndman, Rob J, and George Athanasopoulos

Forecasting: principles and practice: OTexts. Hyndman, Rob J, and Yeasmin Khandakar

Automatic time series for forecasting: the forecast package for R: Monash University, Department of Econometrics and Business Statistics ....

Nerlove, Marc, and Sf Wage

On the optimality of adaptive forecasting. Manage- ment Science 10(2):207-224.

Ord, John Keith, Anne B Koehler, and Ralph D Snyder

Estimation and prediction for a class of dynamic nonlinear statistical models. Journal of the American Statistical As-

sociation 92(440):1621-1629. Pegels, C Carl

Exponential forecasting: some new variations. Man- agement Science:311-315.

Piras, A, and B Buchenel

A tutorial on short term load forcasting. ENGINEER-

ING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEER-ING AND COMMUNICATIONS 7(1):41-47. Price, DHR, and JA Sharp

A comparison of the performance of different univar- iate forecasting methods in a model of capacity acquisition in UK electricity supply. International Journal of Forecasting 2(3):333-348. Proietti, Tommaso

Seasonal heteroscedasticity and trends. Journal of Forecasting 17(1):1-17.

Comparing seasonal components for structural time

series models. International Journal of Forecasting 16(2):247-260. Ramanathan, Ramu, et al.

Short-run forecasts of electricity loads and peaks. In- ternational journal of forecasting 13(2):161-174.

Roken, Rashid Mohammed, and Masood A Badri

Time Series Models for Forecasting Monthly Elec- tricity Peak-Load for Dubai. Chancellor’s Undergraduate Research Award:1-14.

Saab, Samer, Elie Badr, and George Nasr

Univariate modeling and forecasting of energy con- sumption: the case of electricity in Lebanon. Energy 26(1):1-14. Sharp, John A, and David HR Price

Experience curve models in the electricity supply in- dustry. International Journal of Forecasting 6(4):531-540.

Taylor, James W

a Exponential smoothing with a damped multiplicative trend. International journal of Forecasting 19(4):715-725.

b Short-term electricity demand forecasting using dou-

ble seasonal exponential smoothing. Journal of the Operational Re- search Society 54(8):799-805.

Triple seasonal methods for short-term electrici-

ty demand forecasting. European Journal of Operational Research 204(1):139-152.

—2011 Short-term load forecasting with exponentially weighted methods. IEEE Transactions on Power Systems 27(1):458- 464.

Taylor, James W, Lilian M De Menezes, and Patrick E McSharry

A comparison of univariate methods for forecasting electricity demand up to a day ahead. International Journal of Fore- casting 22(1):1-16.

Theil, Henri, and S Wage

Some observations on adaptive forecasting. Manage- ment Science 10(2):198-206.

Winters, Peter R

Forecasting sales by exponentially weighted moving

averages. Management science 6(3):324-342. Zhu, Qing, Yujing Guo, and Genfu Feng

Household energy consumption in China: Forecast- ing with BVAR model up to 2015. 2012 Fifth International Joint Conference on Computational Sciences and Optimization, 2012, pp. 654-659. IEEE.

Publicado
2019-08-13
Cómo citar
S. AL-Farttoosi, S. A., & Mansouri, B. (2019). Predicting Electricity Consumption in Misan Province of Iraq Using Univariate Time Series Analysis. Opción, 35, 1137-1151. Recuperado a partir de https://produccioncientificaluz.org/index.php/opcion/article/view/31578