Proceedings of the International scientific and practical conference ―Science, Technology and Culture in the Era of Globalization‖ (December 24-26, 2025) / Publisher website: www.naukainfo.com. – Geneva, Switzerland, 2026. – 234 p.

201 fluctuations in production, such as in solar and wind power plants. Intelligent dynamic demand response strategies facilitate the highly efficient distribution of energy, preventing overloads and losses, which is especially important for complex multi-tiered grids. Smart grids equipped with such intelligent algorithms are capable of autonomous energy flow management, fault detection, and operational control, which significantly improves overall system reliability and performance. Through federated learning algorithms, such grids can optimize load management even within microgrids, while simultaneously preserving user data privacy and avoiding the centralization of sensitive information. CEAI also plays a significant role in energy storage systems, where it predicts battery degradation patterns and optimizes their use, thereby extending equipment life and improving energy efficiency. Furthermore, AI-based predictive maintenance reduces equipment downtime and lowers maintenance costs, which is critical for the smooth operation of renewable energy sources. The integration of CEAI with blockchain technologies opens up new opportunities for transparent and secure energy trading, in particular through the implementation of smart contracts. This facilitates collaborative energy management among distributed units such as smart homes, electric vehicles, and microgrids, creating decentralized platforms for the efficient exchange and use of energy resources. In microgrid management, CEAI applies advanced methods such as multi-agent deep reinforcement learning (MADRL) and statistical approaches for risk management (CVaR) [9], which allows taking into account uncertainty in energy consumption and generation. Consequently, energy planning and resource allocation become more optimized and adaptive. Furthermore, hybrid Edge-Cloud architectures support local data processing, which enhances the precision of fault diagnostics - for example through thermal image analysis. In smart buildings, CEAI optimizes energy consumption in heating, ventilation, air conditioning (HVAC) and lighting systems by applying real-time data processing