Field test of Model Predictive Control in residential buildings for utility cost savings

Abstract

Air conditioners in residential buildings often rely on local feedback control to regulate the indoor temperature, where the unit is cycled on and off by comparing the actual temperature with the temperature set-point. Model Predictive Control (MPC) is a technique that makes use of building models, disturbance forecasts, and optimization algorithms to enhance building performance. MPC has demonstrated its potential in reducing building energy consumption and operating costs; however, it has seldom been applied in residential buildings because of the high implementation costs, complicated hardware configuration, and insufficient data. In this study, we developed and tested a MPC controller in a real apartment aimed at reducing the operating costs based on the local Time-of-Use (ToU) utility structure. We developed a feasible and easy-to-use hardware configuration as well as a cloud-based software deployment. The MPC controllers were implemented and evaluated through one-month field testing in a bedroom and living room of an apartment in a residential building. The field test showed that the MPC controller saved 22.1% for the bedroom and 26.8% cooling costs for the living room, compared to conventional rule-based control (RBC). We further confirmed the robustness of the MPC controller as the field test showed that the MPC controller is able to outperform the conventional RBC controller, even if the model is not accurate. Our work provides a practical MPC solution that can unlock the load flexibility potential of residential buildings, which creates a win–win solution for the households and the grid: saving utility costs for households and helping the grid to manage the balance between supply and demand.