While the potential of model-based control is recognized, the development of reasonably accurate models for control applications remains a challenging, cumbersome and time-consuming task. This paper proposes a systematic and generalizable approach – based on low-order control-oriented thermal network (RC) archetypes – for the development, testing and implementation of readily scalable control solutions for buildings. These archetypes, focusing specifically on control applications, can significantly facilitate assessing the effect of control strategies on energy efficiency and load management. Furthermore, this approach can also be used for characterization, design and testing of simple retrofit strategies. The utilization of RC-based archetypes for common types of zones (such as those heated/cooled with forced-air or radiant systems) is proposed. These simple models (often 1st to 4th order models suffice), can also be used for the control of residential buildings. For larger buildings, zonal models can be combined to form whole building models.

The data presented here were collected independently for 6 real buildings by researchers of different institutions and gathered in the context of the IEA EBC Annex 81 Data-driven Smart Buildings, as a joint effort to compile a diverse range of datasets suitable for advanced control applications of indoor climate and energy use in buildings. The data were acquired by energy meters, both consumption and PV generation, and sensors of technical installation and indoor climate variables, such as temperature, flow rate, relative humidity, CO₂ level, illuminance. Weather variables were either acquired by local sensors or obtained from a close by meteorological station. The data were collected either during normal operation of the building, with observation periods between 2 weeks and 2 months, or during experiments designed to excite the thermal mass of the building, with observation periods of approximately one week. The data have a time resolution varying between 1 min and 15 min; in some case the highest resolution data are also averaged at larger intervals, up to 30 min.

This paper presents a general methodology to model and activate the energy flexibility of electrically heated school buildings. The proposed methodology is based on the use of archetypes of resistance–capacitance thermal networks for representative thermal zones calibrated with measured data. Using these models, predictive control strategies are investigated with the aim of reducing peak demand in response to grid requirements and incentives. A key aim is to evaluate the potential of shifting electricity use in different archetype zones from on-peak hours to off-peak grid periods. Key performance indicators are applied to quantify the energy flexibility at the zone level and the school building level. The proposed methodology has been implemented in an electrically heated school building located in Québec, Canada. This school has several features (geothermal heat pumps, hydronic radiant floors, and energy storage) that make it ideal for the purpose of this study. The study shows that with proper control strategies through a rule-based approach with near-optimal setpoint profiles, the building’s average power demand can be reduced by 40% to 65% during on-peak hours compared to a typical profile.