Investigating the performance of scenario-based model predictive control of space heating in residential buildings

This paper investigates the performance of scenario-based model predictive control (SB-MPC) for space heating operation to address the inherent uncertainty of weather forecasts and predictions of occupancy. In contrast to existing reported studies, this study relied on a sophisticated meteorological model and a higher order Markov chain occupancy model to generate stochastic disturbance scenarios. When applying the SB-MPC scheme for energy-efficient operation, simulation results suggested a slight increase in energy consumption (from approx. 27.7 kWh/m2 to 28.0 kWh/m2) when using one and 100 disturbance scenarios, respectively, while thermal comfort violations were reduced significantly (from 60°Ch to 10°Ch). Furthermore, the SB-MPC scheme was tailored to provide demand response and thereby achieved cost savings of 16.1% and 13.1% compared to conventional proportional-integral control when considering one and 100 disturbance scenarios, respectively. Choosing the appropriate number of disturbance scenarios thus relies on a consideration of the trade-off between the acceptable thermal comfort violations and energy-related benefits.

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