Experimental insights on CIRLEM: Enhancing energy efficiency and flexibility in buildings

The growing complexity of urban energy systems, climate uncertainties, and geopolitical disruptions highlight the need for energy flexibility and smart management. Recent developments in smart buildings enable real-time adaptability and collective energy behavior through the deployment of Reinforcement Learning (RL), which optimizes energy use, integrates distributed resources, and enhances demand response. However, challenges in communication, system diversity, and user intervention must be addressed for scalable and secure multi-agent RL-based management. This study evaluates the application of CIRLEM, a previously developed and introduced Energy Management system that integrates Collective Intelligence (CI) with an online, value-based, model-free RL algorithm. The experiment is carried out in Building Energy Living Lab in France, as EU Horizon COLLECTiEF’s pilot, equipped with an advanced building management system for one year. The control algorithm interacts with the building management system every 15 min, optimizing setpoints based on real-time monitoring of energy use and indoor environmental conditions. The results indicate an 18% reduction in overall energy use compared to the reference baseline, with heating and cooling demands decreasing by 5% and 32%, respectively. Additionally, peak power demand is curtailed up to 15% for heating and 50% for cooling. The performance of the control algorithm is in an excellent level for more than 50% of the time in 1-month analyses through achieving load reduction and shifting. This experimental study demonstrates that CIRLEM effectively enhances energy flexibility while maintaining thermal comfort, demonstrating its potential for broader implementation, paving the way decentralized energy management solutions in smart buildings and urban energy networks.