Research Outputs
Hygrothermisches Verhalten von Bauteilkonstruktionen und deren Auswirkung auf das Raumklima
Towards non-invasive temperature measurements in HVAC: A characterization and correction approach
The existing building stock within the European Union is responsible for a considerably huge amount of the total energy consumed. This has prompted legislative actions that focus on increasing the efficiency of Heating, Ventilation and Air Conditioning facilities by employing building automation and electronic monitoring systems. The fluid flow temperature in the hydraulic grid of a building is therefore an essential parameter to be measured, where clamp-on temperature sensors are often applied due to their simple and cost-effective installation. As the plumbing industry heads towards non-metal pipe materials with low thermal conductivity, the applicability of non- invasive measurement procedures diminishes. In this context, a characterization approach of non-invasive temperature measurements that is linked to a thermal resistance model is experimentally validated. Based on that, a correction algorithm to reduce the deviation between measured surface and the fluid flow temperature for steady state conditions is derived and tested. The presented approach provides sufficient characterization and correction performance, albeit several limitations have to be taken into consideration.
Monitoring data from an office room in a real operating building, suitable for state-space energy modelling
The dataset provides all necessary variables for data-driven energy modelling of an office room. The measurement data have been obtained from an office building operating as living lab in a temperate climate of Central Europe. The temperatures and the ventilation air flowrate are raw measurements, while the heat flows are calculated from measurements. The incoming solar irradiance is calculated with two façade models –simple and enhanced–, using measurements (solar irradiance, movable shading settings) and building characteristics (geometry, glazing and shading properties). One year and four months of data is provided with a fine one-minute time step and a coarser fifteen-minute time step. The dataset can be used to test and validate data-driven models, for example for predictive control applications.
Validation of scale - adaptive and elliptic relaxation turbulence models applied to flow around buildings
Complex glass facade modelling for Model Predictive Control of thermal loads: impact of the solar load identification on the state-space model accuracy
Above and beyond improving the efficiency of the building envelope and the energy supply system, the demand-side flexibility in terms of load shifting and peak reduction are vital factors in further increasing the share of volatile renewable energy sources. The thermal activation of building components, like floors and ceilings, enables the cost-effective potential for short-term energy storage to fulfil these requirements. In order to exploit the storage capabilities of active building systems, a reliable model predicted control (MPC) approach is required. However, primarily if a large glass façade element is utilised, the appropriate modelling of solar loads is critical for an effective MPC operation. Hence, based on a dynamic building simulation tool, a characteristic map for the solar load prediction of a glass façade system in combination of external venetian blinds was generated to enhance the state-space model approach for the MPC algorithm. The comparison with a conventional state-space model approach shows the integration of a detailed characteristic map can only marginally improve the prediction accuracy. The additional information required from the glass façade manufacturer and the associated simulation effort is not of substantial value. In contrast, the conventional grey box model enables an entirely datadriven parameter identification, without the manufacturers’ data. Furthermore, the MPC optimisation procedure, searching for the best control strategy, can be more efficient (solver-based optimisation), with shorter computing turnaround times.