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

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Abstract

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

engineering and technology > informatics
natural sciences > environmental science
social sciences > economic science(s)

Faculty

Not relevant

Institution

Soproni Egyetem

Item Type:	Book Section
SWORD Depositor:	Teszt Sword
Depositing User:	Csaba Horváth
Identification Number:	MTMT:36063678
Date Deposited:	01 Apr 2025 10:41
Last Modified:	01 Apr 2025 10:41
URI:	http://publicatio.uni-sopron.hu/id/eprint/3570

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