Supervised learning based iterative learning control platform for optimal HVAC start-stop in a real building context

In comparison to commonly employed iterative learning controls and reinforced learning techniques in model predictive controls for buildings, a supervised learning based iterative learning control platform that is more suitable and computationally efficient for real-world applications is proposed. The proposed control system relies on a data-driven model and utilizes the Random Forest algorithm to develop an HVAC start-stop model; this model considers only a limited system history period that can influence the current state, thus avoiding prolonged learning periods and time-consuming exploration. Specifically, within the current timeframe, the HVAC start-stop model learns from daily errors, and start and stop times "labeled as adjusted" accordingly. The proposed platform was validated against the TRNSYS baseline of a research facility, which was meticulously calibrated with actual measurements. In comparison with the convention, the proposed approach yielded significant energy savings of 6.5-7.6 % in HVAC annual energy consumption, while maintaining temperature comfort for approximately 97-98 % of the annual operating days. Notably, by implementing supply air volume ramp-up in conjunction with HVAC optimal start control, temperature comfort for up to 99 % of the annual operating days was achieved, along with a notable 9.7 % reduction in HVAC annual energy consumption.