In this blog, we show how room temperature optimization responds to different building envelopes and the availability of economic cooling. We continue to explore the capability of this optimization in simulated examples. For context, please read my past blog articles Simulation a Safe Playground for Smart HVAC Control and Towards a Zero HVAC Energy Building and Beyond.
In these three simulations (shown as an animation), the amount of heat transfer between the room and the outside air is set to zero, medium and high. Every other aspect is identical between the three simulations. In particular, in all three simulations, the HVAC load is optimized and shifted according to Time of Use (TOU) tariff. However, the percentage shift in HVAC load is different. When the heat transfer is zero, the cooling load is shifted completely to the cheapest time of day (as allowed by the comfort range). When the heat transfer is medium and high, the cooling load is still shifted but the percentage of shift is less than before. This is because:
Economic Cooling is a common form of air side optimization. When the condition is right, the BMS control logic brings in more cold outside air to mix with return air so that the chiller doesn’t have to work as hard to bring the temperature of this mixed air down to the supply air temperature setpoint. Our optimization is aware of this “free” cooling and the strategy is to set a low room temperature setpoint to maximize economic cooling (when this option demands less overall energy/cost).
The way we utilize economic cooling is particularly interesting because it’s a good example on how different parts in our system work together. There are three layers in this story:
It really takes layer No.3 to fully utilize the benefit of economic cooling. Furthermore, there is an advantage in optimizing room temperature setpoint compared to other setpoints in the HVAC system: By optimizing room temperature, we can prioritize or deprioritize comfort and this is the goal of any HVAC system.
In order to optimize room temperature, an HVAC model is fitted to real building data relating power to room temperature and various other things. The model fit process can estimate thermal dynamics of the building, properties of HVAC equipments and HVAC control logics implemented in the BMS. Alternatively, we can specify some parameters and let the model fit process figure out the rest. One interesting HVAC equipment parameter is the temperature derating of the coefficient of performance (COP) of chiller. We will look at how the optimization respond to this in the next blog.
Dr. Rui (Ray) Xu is Data Scientist at BuildingIQ. During his career, he has encountered many challenges in the transfer of human knowledge into machines, the interpretation of results of algorithms into human intuition, and the verification of evolving strategies. This experience has helped him to solve some of the most difficult and interesting problems in the industry. He describes himself as a bit quiet but also very energetic and assertive when there is a mathematical, simulated and/or experimental proof behind the scenes.