METHODS OF INCREASING THE EFFICIENCY OF ENERGY INFRASTRUCTURE MANAGEMENT USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES

Abstract

This paper examines contemporary approaches to improving the efficiency of energy infrastructure management using artificial intelligence technologies. The study analyses methods for  enhancing the efficiency of energy infrastructure management using artificial intelligence technologies. It has been established that the integration of machine learning algorithms, deep neural networks, multi-threaded agent-based systems and big data analytics makes it possible to improve the accuracy of generation and load forecasting, optimise energy distribution in networks and reduce operational costs. The results of the study demonstrate that the application of AI facilitates the transition from reactive to proactive management of the energy system. An analysis was conducted of the potential for using machine learning, deep neural networks, multi-threaded agent systems and big data analytics in various segments of energy systems: generation, transmission and distribution, energy consumption and system management. It is shown that the integration of AI allows for improved accuracy in forecasting generation and load, optimisation of energy flows, reduction of operational costs, and increased reliability of energy supply. Particular attention is paid to the application of AI in renewable energy systems. This is due to the intermittency of electricity generation and the need for adaptive control and real-time resource balancing. It has been established that predictive maintenance and intelligent data analysis technologies can reduce equipment downtime, improve grid stability and lower the risk of emergencies. The interaction of digital technologies, such as the Internet of Things and blockchain, with AI is examined in the context of the development of smart energy systems and distributed generation. The article emphasises that the effective integration of AI into the energy sector requires a comprehensive approach that takes into account technical, economic, social and regulatory aspects. The role of digital and regulatory tools in ensuring the successful integration of AI into the energy sector is highlighted. It is found that the comprehensive implementation of technologies requires consideration of technical, economic and socio-organisational aspects, including data exchange standards, cybersecurity and the enhancement of staff digital literacy. Thus, the application of artificial intelligence in energy systems not only enhances the efficiency and flexibility of management but also lays the foundation for the development of adaptive, resilient and economically optimised energy infrastructures capable of meeting the current challenges and requirements of renewable energy development. The study confirms that the implementation of AI contributes to the formation of adaptive, flexible and resilient energy infrastructures capable of responding effectively to fluctuations in supply and demand, ensuring economic efficiency and the integration of renewable energy sources.