Assessment of thermal comfort at the building level: Evaluation of aggregation methods with a Danish case study of a campus building

Six methods for aggregating local thermal comfort scores in six offices to a single global score are investigated. using data collected by the building management system in a campus building of Aalborg University. Three static: 1) number of rooms weighted mean, 2) area-weighted mean, 3) desk-weighted mean, and three dynamic: 4) simple occupancy-weighted mean (PIR sensors), 5) advanced occupancy-weighted mean (PIR sensors mixed with the number of desks), 6) number of the occupants-weighted mean (camera readings). A notable disparity emerged between static methods, which rely solely on fixed parameters, and dynamic methods, which account for time-dependent factors over short timeframes. Dynamic methods consistently yielded lower global scores, irrespective of individual room performance. The difference can be up to 15% monthly. The PIR sensors, which are now commonly used in office or education buildings to control artificial lighting are a good indication of the occupancy (only present and not present). The information on number of occupants in the offices, collected by installed cameras, did not provide significantly better results in the analysed case study

A novel Monte Carlo modelling method to support control strategies development in building ventilation

Ventilation is critical for maintaining thermal comfort and air quality in buildings. However, developing ventilation control is challenging due to the large number of control variables and performance criteria. Typical ventilation controls are On-Off controls, time schedules, and PI/PID controls. Specific parameters are tuned based on simple rules of thumb and the engineer’s experience. Although building simulation tools are commonly applied, they are normally used to evaluate the performance of certain control strategies rather than guide the development of these control strategies. This study presents a novel Monte Carlo modelling method that supports the early-stage development of ventilation control. The method consists of the following steps: (1) Creating an initial building model, (2) Identifying relevant control variables and assigning probability distributions, (3) Executing Monte Carlo simulations, (4a) Applying filters to assess the outcomes, (4b) Performing sensitivity analysis on control variables, (5) Selecting a ventilation control strategy fulfilling control objectives. The method is tested on a classroom equipped with a hybrid ventilation system. The case study demonstrates that the novel approach, allows ventilation designers to systematically identify high-performance control solutions for multiple control variables and performance requirements. Thus, offering clear advantages over the traditional trial-and-error method

Do the customers remember?:The fade-out effect from the demand response applied in the district heating system in Denmark

Buildings can deliver short-term thermal energy storage to energy systems. In district heating (DH) systems, it is mainly desk studies and simulations that reveal a large thermal flexibility potential. Knowledge from real-life case studies on how residents participate in demand management campaigns is crucial for the successful utilisation of buildings’ flexibility potential for minimizing bottlenecks in the daily operation of DH systems. In the field study including 72 single-family houses connected to the 3GDH network in southern Denmark, the demand response (DR) strategy “night setback” was applied for two heating periods. The houses were equipped with control and monitoring equipment, which allowed the deactivation of the heating system while monitoring the indoor temperature, so it does not drop below the defined value. The occupants controlled the DR events settings and could at any time stop utilisation of the night setback strategy (implicit participation in the DR). All 72 houses applied the night setback during both heating periods. Yet, the participation time decreased from 89% to 81%. The lowest participation rate was noted for the farm house, 60% and 9% of heating periods 1 and 2, respectively. In around 60% of the DR events, the night setback strategy was activated at 20:00