Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads

Increasing energy efficiency of thermostatically controlled loads has the potential to substantially reduce domestic energy demand. However, optimizing the efficiency of thermostatically controlled loads requires either an existing model or detailed data from sensors to learn it online. Often, neither is practical because of real-world constraints. In this paper, we demonstrate that this problem can benefit greatly from multi-agent learning and collaboration. Starting with no thermostatically controlled load specific information, the multi-agent modelling and control framework is evaluated over an entire year of operation in a large scale pilot in The Netherlands, constituting over 50 houses, resulting in energy savings of almost 200 kW h per household (or 20% of the energy required for hot water production). Theoretically, these savings can be even higher, a result also validated using simulations. In these experiments, model accuracy in the multi-agent frameworks scales linearly with the number of agents and provides compelling evidence for increased agency as an alternative to additional sensing, domain knowledge or data gathering time. In fact, multi-agent systems can accelerate learning of a thermostatically controlled load’s behaviour by multiple orders of magnitude over single-agent systems, enabling active control faster. These findings hold even when learning is carried out in a distributed manner to address privacy issues arising from multi-agent cooperation.