The Use of Artificial Intelligence Technologies in Energy and Climate Security - Igbal A. Guliev; Agil Mammadov; Kanan Ibrahimli (Review of Business and Economics Studies)
This study provides a theoretical analysis of the use and application of artificial intelligence (AI) in the energy sector as it relates to climate security. The object of the study is energy and climate security as types of economic activity and social activity. The subject of the research is artificial intelligence in relation to the object area of research. The purpose of the study is to create a sound scientific basis for the use of artificial intelligence in the energy sector, as well as to identify emerging problems in the formation of a science-based approach to climate policy development. The authors' research includes three interrelated research methodologies: topic modeling, text mining as part of qualitative analysis and object modeling as part of the systematization of results that are adequate to the subject area of the study and correspond to their reality; in addition, the authors supplemented the quantitative results with a theoretical and heuristic analysis of the scientific results of other researchers. The concept of parametric optimization (PO) is used as an effective method for solving the applied problem of testing the hypothesis of managing energy costs and energy efficiency based on AI in order to achieve optimal performance of the technical system and compliance with the Sustainable Development Goals (SDGs) in the field of climate security. The study's findings suggest that AI is becoming fundamental to the development of a modern energy sector based on data and complex relationships and provides tools to improve technical system performance and efficiency in the face of sanctions restrictions. The authors conclude that the truth of the hypothesis has been proven: the use of AI as a control feedback loop at a technical facility for purification and energy generation is a more cost-effective and technically optimal alternative to a “live” operator, which will eliminate the human error factor. In this regard, the energy industry, utilities, grid operators and independent power producers must pay special attention to the introduction of AI technologies into existing technical systems.