Model Predictive Control of Residential Baseboard Heaters with Distributed System Architecture

Typical residential HVAC systems employ mechanical or hard-coded deadband control behaviors that are unresponsive to changing energy costs and weather conditions. In this paper, we investigate the potential of electric baseboard heaters to maintain a comfortable temperature while optimizing electricity consumption given weather forecasts and price data. We first propose a distributed system architecture that utilizes mobile application platforms. We then develop, assemble, and deploy a sensor network and Internet server to collect real-time temperature data from an apartment. With these sensor streams, we identify a thermal model of the apartment. Finally, we propose a model predictive control algorithm and perform a software-in-the-loop simulation of the cloud-based system to demonstrate the economic advantage.