Forecasting electricity consumption in Algeria using artificial neural networks
Joint Authors
Shukri, Sidi Muhammad
Sahid, Abd al-Qadir
Source
Issue
Vol. 12, Issue 1 (30 Jun. 2022), pp.247-261, 15 p.
Publisher
University of Echahid Hamma Lakhdar-el-Oued Faculty of Economic Commercial and Management Sciences
Publication Date
2022-06-30
Country of Publication
Algeria
No. of Pages
15
Main Subjects
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