LAUSR

Abstract Previous studies have identified significant demand response (DR) potentials in using economic model predictive control (E-MPC) of space heating to exploit the inherent thermal mass in residential buildings for short-term energy storage. However, the economically viable realisation of E-MPC in residential buildings requires an effort to minimise the need for additional equipment and labour-intensive modelling processes. This paper reports on an experiment where a novel E-MPC setup was used for thermostatically control of a hydronic radiator in a highly-insulated residential building located on the NTNU Campus in Trondheim, Norway. The E-MPC utilized data from a heating meter, two temperature sensors and an existing weather forecast web service to train a linear black-box model. The results showed that the precision of model trained on excitation data that was generated using setpoints of either 21 or 24 °C was sufficient to obtain good control of the indoor air temperature while shifting consumption from high to low price periods. The findings of the experiment indicate that a minimal E-MPC setup is able to realize the significant DR potential that lies in utilizing the inherent thermal mass in residential buildings.