Room-Level Indoor Temperature Forecasting Using a Grey-Box Model with Dynamic Thermostat Offset and TRV-Controlled Heating

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Abstract
Accurate forecasting of indoor temperature is essential for effective climate control in buildings. However, existing models used in Model Predictive Control (MPC)-based demand response applications often do not fully take into account key dynamic factors, particularly those introduced by heat emission system(s), weather variability, and occupancy. One critical limitation is the treatment of thermostatic radiator valve (TRV) dynamics and the discrepancy between the temperature perceived by occupants (i.e. the actual indoor temperature) and that measured by the TRV. This discrepancy is typically approximated by a fixed offset, despite evidence that it varies over time and depends on radiator activity, weather, and occupancy. In this paper, a novel dynamic grey-box model is proposed for room-level temperature forecasting that explicitly accounts for four key contributors, namely outdoor air temperature, solar radiation, TRV-controlled radiator heating and building envelope characteristics. The model introduces separate dynamic states for TRV temperature and actual room temperature, enabling the model to capture the time-varying offset while respecting the causal thermostatic control structure, where radiator actuation depends solely on the TRV temperature. The results demonstrate substantial improvements in forecasting accuracy, highlighting the model’s suitability for advanced control strategies aimed at reducing energy use for space heating while maintaining thermal comfort for occupants.