State of the art in energy consumption using deep learning models
Shikha Yadav,
Nadjem Bailek,
Prity Kumari,
Alina Cristina Nuţă,
Aynur Yonar,
Thomas Plocoste,
Soumik Ray,
Binita Kumari,
Mostafa Abotaleb,
Amal H. Alharbi,
Doaa Sami Khafaga,
El-Sayed M. El-Kenawy
Affiliations
Shikha Yadav
Miranda House, Department of Geography, University of Delhi, Delhi, India
Nadjem Bailek
Energies and Materials Research Laboratory, Faculty of Sciences and Technology, University of Tamanghasset, Tamanrasset 10034, Algeria
Prity Kumari
College of Horticulture, Anand Agricultural University, Anand 388110, India
Alina Cristina Nuţă
School of Economics and Business Administration, Danubius University, Galaţi, Romania
Aynur Yonar
Department of Statistics, Faculty of Science, Selçuk University, Konya, Türkiye
Thomas Plocoste
Department of Research in Geoscience, KaruSphère Laboratory, 97139 Abymes, Guadeloupe, France
Soumik Ray
Department of Agricultural Economics and Statistics, Centurion University of Technology and Management, Paralakhemundi, Odisha 761211, India
Binita Kumari
Department of Agricultural Economics, Rashtriya Kisan (PG) College, Shamli, India
Mostafa Abotaleb
Department of System Programming, South Ural State University, Chelyabinsk, Russia
Amal H. Alharbi
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Doaa Sami Khafaga
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
El-Sayed M. El-Kenawy
Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
In the literature, it is well known that there is a bidirectional causality between economic growth and energy consumption. This is why it is crucial to forecast energy consumption. In this study, four deep learning models, i.e., Long Short-Term Memory (LSTM), stacked LSTM, bidirectional LSTM, and Gated Recurrent Unit (GRU), were used to forecast energy consumption in Brazil, Canada, and France. After a training test period, the performance evaluation criterion, i.e., R2, mean square error, root mean square error, mean absolute error, and mean absolute percentage error, was performed for the performance measure. It showed that GRU is the best model for Canada and France, while LSTM is the best model for Brazil. Therefore, the energy consumption prediction was made for the 12 months of the year 2017 using LSTM for Brazil and GRU for Canada and France. Based on the selected model, it was projected that the energy consumption in Brazil was 38 597.14–38 092.88, 63 900–4 800 000 GWh in Canada, and 50 999.72–32 747.01 GWh in France in 2017. The projected consumption in Canada was very high due to the country’s higher industrialization. The results obtained in this study confirmed that the nature of energy production will impact the complexity of the deep learning model.
