Low-Cost HVAC Control Rivals Advanced Algorithms

Researchers at an unspecified institution develop two new heating and cooling system controllers that require minimal setup and achieve near-optimal energy efficiency. The controllers, designed for objectives linked to thermal load like cost and pollution, need only two thermal parameters to operate.

Simulations demonstrate the controllers maintain occupant comfort while reaching 43 to 98% of the performance gains possible with complex control methods like model predictive control and reinforcement learning. Importantly, the system proved robust even with imprecise parameter inputs, suggesting it could be deployed without extensive calibration. This work indicates that simpler control strategies can deliver substantial benefits without the data requirements and computational demands of more sophisticated approaches.