Application of AI and Machine Learning for Energy Efficiency to Drive Sustainable Economics: Possible Implications in Azerbaijan

Mammadli, Kanan (2025) Application of AI and Machine Learning for Energy Efficiency to Drive Sustainable Economics: Possible Implications in Azerbaijan. In: FENNTARTHATÓSÁGI ÁTMENET – INNOVÁCIÓS ÖKOSZISZTÉMÁK – DIGITÁLIS MEGOLDÁSOK: Konferenciakötet. Soproni Egyetem Kiadó, Sopron, pp. 625-636. ISBN 9789633345504

Szöveg MTU_2024_Conf_Proceedings_SOE_LKK_pp.625-636_Mammadli.pdf Download (1MB)

Absztrakt (kivonat)

According to a recent International Energy Agency report, more than half of worldwide energy production goes to waste within various stages from production to consumption. Energy optimization is an important topic today, not just for economic reasons but also to promote green energy and zero-emissions goals. This article examines utilizing modern AI technologies as a solution for efficiency, which leads to economic growth in countries where energy production is a significant component of the economy. These artificial intelligence systems could help to detect and predict system breakdowns, as well as evaluate consumption patterns to optimize efficiency. At the same time, the paper demonstrates importance of the Sustainable Economics in developing countries and the potential economic benefits, environmental impact and enhanced energy security that AI will contribute. Case studies from other regions are adapted to Azerbaijan’s scenario, potentially providing a solution in the future. Possible obstacles such as policymaking, human resources, and the cost of the implication, have been identified, as have opportunities. The findings demonstrate that the impacts of AI, particularly in the oil and gas sector, will make an important contribution to Azerbaijan's sustainable economics.

Tudományterület / tudományág

műszaki tudományok > informatikai tudományok
természettudományok > környezettudományok
társadalomtudományok > közgazdaságtudományok

Kar

Nem releváns

Intézmény

Soproni Egyetem

Mű tipusa:	Könyv része
SWORD Depositor:	Teszt Sword
Felhasználó:	Csaba Horváth
A mű MTMT azonosítója:	MTMT:36063678
Dátum:	01 Ápr 2025 10:41
Utolsó módosítás:	01 Ápr 2025 10:41
URI:	http://publicatio.uni-sopron.hu/id/eprint/3570

Actions (login required)

Tétel nézet