Forecasting electricity consumption in Algeria using artificial neural networks

Joint Authors

Shukri, Sidi Muhammad
Sahid, Abd al-Qadir

Source

Economic Visions Review

Issue

Vol. 12, Issue 1 (30 Jun. 2022), pp.247-261, 15 p.

Publisher

University of Eloued Faculty of Economics Commercial and Management Sciences

Publication Date

2022-06-30

Country of Publication

Algeria

No. of Pages

15

Main Subjects

Economy and Commerce

Topics

Abstract EN

This paper applied the artificial neural network (ANN) to forecast electricity consumption in Algeria.

two independent variables, GDP (gross domestic product) per capita and population, are used to forecast electricity consumption.

the models' performance is evaluated using the coefficient of determination (R2) and the mean absolute percentage error (MAPE).

the results show that the ANN model that models electricity consumption as a function of economic indicators outperforms the ANN time input model.

in addition, the results indicate that Algeria’s projected electricity consumption will be 76.06 and 94.66 billion kwh in 2020 and 2025, respectively.

as a result, improved electricity forecasting is critical for policymakers when constructing future energy plants.

American Psychological Association (APA)

Shukri, Sidi Muhammad& Sahid, Abd al-Qadir. 2022. Forecasting electricity consumption in Algeria using artificial neural networks. Economic Visions Review،Vol. 12, no. 1, pp.247-261.
https://search.emarefa.net/detail/BIM-1424132

Modern Language Association (MLA)

Shukri, Sidi Muhammad& Sahid, Abd al-Qadir. Forecasting electricity consumption in Algeria using artificial neural networks. Economic Visions Review Vol. 12, no. 1 (Jun. 2022), pp.247-261.
https://search.emarefa.net/detail/BIM-1424132

American Medical Association (AMA)

Shukri, Sidi Muhammad& Sahid, Abd al-Qadir. Forecasting electricity consumption in Algeria using artificial neural networks. Economic Visions Review. 2022. Vol. 12, no. 1, pp.247-261.
https://search.emarefa.net/detail/BIM-1424132

Data Type

Journal Articles

Language

English

Notes

Includes appendices : p. 255-261

Record ID

BIM-1424132