We designed a Cloud-Based RL algorithm that continuously learns how to optimize power consumption by remotely reading the environmental data and consequently defining the HVAC set-points. In essence, MPC can fit complex thermodynamics and achieve excellent results in terms of energy savings on a single building. The main drawback of RBC is that they are difficult to be optimally tuned because they are not adaptable enough for the intrinsic complexity of the coupled building and plant thermodynamics. Therefore, it is desirable to introduce RL controls for large-scale applications on HVAC systems where the operating cost is high, like those in charge of the thermo-regulation of a significant volume. Supermarkets are, by definition, widespread buildings with variable thermal loads and complex occupational patterns that introduce a non-negligible stochastic component from the HVAC control point of view.

The need for Energy Savings has become increasily foundamental to fight Climate Change. We have been working on a cloud-based RL algorithm that can retrofit existing HVAC controls to obtain substantial results. In the last decade, a new class of controls which relies on Artificial Intelligence have been proposed. In particular, we are going to highlight data-driven controls based on Reinforcement Learning (RL), since they showed from the very beginning promising results as HVAC controls [2]. There are two main ways to upgrade with RL the air conditioning systems: to implement RL on new systems or to retrofit the existing ones.