A high electricity bill arrives. You ask your smart-home assistant what happened. It checks the meter data, explains that the electric-vehicle charger ran during peak-rate hours, and recommends a cheaper schedule. Useful. Then you ask how much the new schedule will save next month. The assistant retrieves the tariff, forecasts consumption, applies export credits from the solar panels, and confidently reports a number. ...
TL;DR for operators HVAC optimisation is not really about “setting the right temperature”. That is the version suitable for brochure copy and mildly insulting procurement decks. The harder problem is deciding when comfort, occupancy, outdoor conditions, and electricity prices should overrule one another. The paper behind this article proposes a human-in-the-loop reinforcement learning controller for HVAC systems.1 Its main idea is simple enough to be useful: when occupants override the system, that feedback should not merely fix the current moment. It should also teach the controller what went wrong, so future decisions require fewer overrides. ...