Xing Lu’s scientific contributions

Efficient electrification of space heating/cooling presents the most viable pathway to GHG emissions reduction, and heat pumps (HPs) remain the dominant alternative for replacing gas/oil-based space heating systems. To achieve widespread adoption of HPs, it is imperative to improve their energy efficiency and operational cost. In this paper, a scalable and computationally inexpensive advanced predictive rule-based-control (PRBC) strategy for HPs is presented. The controller is tested on an Energy Plus prototype model of a single-family detached house within the building optimization testing framework (BOPTEST). The HVAC system consists of a single-speed HP, inclusive of a single-speed DX heating coil, a single-speed DX cooling coil, and a constant-speed fan. The PRBC model uses the current indoor air temperature inside the building, day-ahead ambient air temperature, and hourly electricity price (HEP) forecasts to preheat/precool a building, with the final goal ofHVAC cost/energy reduction without a noticeable increase of indoor thermal discomfort. The ambient air temperature and HEP forecasts are integrated into the PRBC model by: (i) assigning proportional weight to the forecasted values, prioritizing closer time steps to the present, due to the intuitive principle that forecasting accuracy diminishes with greater temporal distance from the present, (ii) modulating the amount of precooling/preheating based on weighted ambient air temperature and HEP forecasts to not only shift HVAC energy usage from high to low HEP periods but also avoid excess precooling/preheating. Results show the advanced PRBC of being able to identify and quickly respond to finer trends in HEP and ambient temperature than the other controllers resulting in cost/energy savings. The thermal discomfort of the advanced PRBC is comparable to the other controllers, proving the efficacy of the proposed PRBC injudiciously preheating/precooling the building. The advanced PRBC performs significantly better in the cooling season than the heating season, achieving as high as 14%, 9%, and 8% in monthly cost savings, and 11%, 6%, and 8% in monthly HVAC energy savings, as compared to the industry standard, relaxed baseline and literature inspired controllers respectively.