Energy management remains one of the most critical challenges facing the global community. Currently, the lack of a unified software framework capable of simultaneously orchestrating production and consumption leads to significant energy dissipation and grid inefficiencies. This paper introduces EnerOS, an integrated software ecosystem designed to monitor and manage energy flows in real-time. By synthesizing data from diverse renewable energy sources with real-time meteorological and satellite insights, EnerOS autonomously determines the optimal balance between immediate energy consumption and long-term storage (e.g., battery systems or hydrogen conversion). To maximize efficiency, the framework employs predictive analytics at the micro-level—analyzing consumption patterns of individual households and industrial facilities—to forecast localized demand and prevent energy waste. Our simulations suggest that EnerOS can significantly enhance grid stability while reducing the overall carbon footprint, providing a scalable solution for the transition to a sustainable energy future.
GRIGORIOS IOSIFIDIS (2026) studied this question.