Yeah that's a good way to do it. I think there are a couple of additional ideas that might be worth exploring: - Comfort is a function of multiple factors, not

Yeah that's a good way to do it. I think there are a couple of additional ideas that might be worth exploring: - Comfort is a function of multiple factors, not just the dry bulb temperature difference. It includes occupant metabolism, insulation from clothing, radiant heat exchange, air speed, and humidity. This is trivial to solve in the case of a single-person office (just give them control of the window), but more complex once you have multiple occupants with different needs, who are in different locations in the office. I wonder if there's value in using reinforcement learning to customize natural ventilation to optimize the comfort of multiple occupants. - Predicting the temperature the next day, and using a fan to draw in cold night time air into your HVAC ducts when a hot morning/afternoon is expected. This way you can have a reservoir off cool air you can access the next day. - Once you have multiple rooms, and a tall building, there's multiple effects that are worth attempting to coordinate. For example you could coordinate the ventilation of the building air with the stack effect and wind-driven air through the windows, or coordinate the cross-ventilation horizontally through multiple rooms. This would not just modulate temperatures, but also satisfy indoor air quality needs traditionally achieved through the HVAC. Typically a lot of fine-tuning of natural ventilation isn't done because the actual energy/comfort impact for one of these ideas is small relative to complexity of building the system, and the large uncertainty associated with the dynamics of airflow. But my general hypothesis is that with the integration of cheap prediction or RL methods, we can reduce that uncertainty which would permit a more long-tail approach where we can combine multiple small improvements.