ВИКОРИСТАННЯ ШТУЧНОГО ІНТЕЛЕКТУ В ЕНЕРГОМЕНЕДЖМЕНТІ ПРОМИСЛОВИХ ПІДПРИЄМСТВ

The article presents the results of a study focused on the implementation of artificial intelligence (AI) technologies in the energy management systems of industrial enterprises. The topic's relevance stems from increasing demands for efficient energy use in the context of economic decarbonization, the development of decentralized energy systems, and the growing role of renewable energy sources. As some of the largest energy consumers, industrial enterprises require innovative approaches to managing energy consumption. The use of AI in this context is viewed as a strategically important direction for improving energy efficiency, reducing operational costs, and ensuring production flexibility. The study aims to provide a comprehensive analysis of AI application opportunities in energy management, identify implementation barriers, and develop a framework model for the integration of intelligent technologies into enterprise energy management systems. The article systematizes key areas of AI use—namely, predictive maintenance of equipment, energy consumption optimization, participation in demand response programs, integration of renewables, and anomaly detection. It offers an overview of current technologies, such as machine learning, deep neural networks, reinforcement learning, and big data analytics. The proposed implementation framework addresses the need for digital infrastructure, data governance systems, qualified personnel, integration with existing IT/OT solutions, and the development of policies for ethical AI use. The scientific novelty lies in the formalization of AI integration stages into the energy management of industrial enterprises and in highlighting the potential of AI to strengthen the role of enterprises as active participants in a decentralized energy system. The study's practical significance lies in the potential use of the proposed framework as a guideline for enterprises in the digital transformation of their energy consumption management systems.