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A Stochastic Estimation Framework for Yearly Evolution of Worldwide Electricity Consumption

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  • Qasem Abu Al-Haija

    (Department of Data Science & Artificial Intelligence, Faculty of Information Technology, University of Petra, Amman 1196, Jordan)

Abstract

The determination of electric energy consumption is remarked as one of the most vital objectives for electrical engineers as it is highly essential in determining the actual energy demand made on the existing electricity supply. Therefore, it is important to find out about the increasing trend in electric energy demands and use all over the world. In this work, we present a prediction scheme for the progression of worldwide aggregates of cumulative electricity consumption using the time series of the records released annually for the net electricity use throughout the world. Consequently, we make use of an autoregressive (AR) model by retaining the best possible autoregression order recording the highest regression accuracy and the lowest standardized regression error. The resultant regression scheme was proficiently employed to regress and forecast the evolution of next-decade data for the net consumption of electricity worldwide from 1980 to 2019 (in billion kilowatt-hours). The experimental outcomes exhibited that the highest accuracy in regressing and forecasting the global consumption of electricity is 95.7%. The prediction results disclose a linearly growing trend in the amount of electricity issued annually over the past four decades’ observation for the global net electricity consumption dataset.

Suggested Citation

  • Qasem Abu Al-Haija, 2021. "A Stochastic Estimation Framework for Yearly Evolution of Worldwide Electricity Consumption," Forecasting, MDPI, vol. 3(2), pages 1-11, April.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:2:p:16-266:d:528336
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    References listed on IDEAS

    as
    1. Yi-Chung Hu, 2017. "Electricity consumption prediction using a neural-network-based grey forecasting approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1259-1264, October.
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