Reinforcement Learning for Optimizing Renewable Energy Utilization in Buildings: A Review on Applications and Innovations DOI Creative Commons
Panagiotis Michailidis, Iakovos Michailidis, Elias B. Kosmatopoulos

и другие.

Energies, Год журнала: 2025, Номер 18(7), С. 1724 - 1724

Опубликована: Март 30, 2025

The integration of renewable energy systems into modern buildings is essential for enhancing efficiency, reducing carbon footprints, and advancing intelligent management. However, optimizing RES operations within building management introduces significant complexity, requiring advanced control strategies. One branch algorithms concerns reinforcement learning, a data-driven strategy capable dynamically managing sources other subsystems under uncertainty real-time constraints. current review systematically examines RL-based strategies applied in BEMS frameworks integrating technologies between 2015 2025, classifying them by algorithmic approach evaluating the role multi-agent hybrid methods improving adaptability occupant comfort. Following thorough explanation rigorous selection process—which targeted most impactful peer-reviewed publications from last decade, paper presents mathematical concepts RL RL, along with detailed summaries summary tables integrated works to facilitate quick reference key findings. For evaluation, outlines different attributes field considering following: methodologies RL; agent types; value-action networks; reward functions; baseline approaches; typologies. Grounded on findings presented evaluation section, offers structured synthesis emerging research trends future directions, identifying strengths limitations

Язык: Английский

Reinforcement Learning for Optimizing Renewable Energy Utilization in Buildings: A Review on Applications and Innovations DOI Creative Commons
Panagiotis Michailidis, Iakovos Michailidis, Elias B. Kosmatopoulos

и другие.

Energies, Год журнала: 2025, Номер 18(7), С. 1724 - 1724

Опубликована: Март 30, 2025

The integration of renewable energy systems into modern buildings is essential for enhancing efficiency, reducing carbon footprints, and advancing intelligent management. However, optimizing RES operations within building management introduces significant complexity, requiring advanced control strategies. One branch algorithms concerns reinforcement learning, a data-driven strategy capable dynamically managing sources other subsystems under uncertainty real-time constraints. current review systematically examines RL-based strategies applied in BEMS frameworks integrating technologies between 2015 2025, classifying them by algorithmic approach evaluating the role multi-agent hybrid methods improving adaptability occupant comfort. Following thorough explanation rigorous selection process—which targeted most impactful peer-reviewed publications from last decade, paper presents mathematical concepts RL RL, along with detailed summaries summary tables integrated works to facilitate quick reference key findings. For evaluation, outlines different attributes field considering following: methodologies RL; agent types; value-action networks; reward functions; baseline approaches; typologies. Grounded on findings presented evaluation section, offers structured synthesis emerging research trends future directions, identifying strengths limitations

Язык: Английский

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