Quantifying the impact of external and internal factors and their interactions on thermal load behaviour of a building

For the energy-efficient design of district heating networks, knowledge about the neighborhood heat load behavior, through heating load profiles in high temporal and spatial resolution, is crucial. Due to the high effort required for transient calculations, a less complex method is needed at the neighborhood level. For this reason, a method is developed, which identifies the relevant parameters influencing the building heating load behavior. Taking these parameters into account, a simple method for heating load profiling is developed using a machine learning algorithm. For this purpose, a parameter study is conducted using dynamic thermal building simulation software. Different parameters influencing the building heating load behavior are varied. To determine the strength of the influence of the individual parameters on the building heating load, to check whether the influence of the parameters is constant or varies over the year and whether parameters are missing here, the results of the parameter study are evaluated statistically. First results show promising results in the detection of the significant parameters, for the creation of a model based on a machine learning algorithm, and the possibility of quantifying their impact on building heating load behaviour

Assessment of five control strategies of an adjustable glazing at three different climate zones

The energy demand for operating modern office spaces is often driven by either the annual heating demand, cooling demand or the demand for electrical lighting. The irradiation of the sun directly and indirectly affects the demand of all three. Consequently, the glazing of higher office buildings is often treated with coating that allows a fixed transmittance. Due to changing exterior conditions and interior needs, a fix-transmittance value is a compromise and most often doesn’t provide optimal thermal and visual conditions. The team in the research project named Fluidglass develops a new glazing in which the transmittance of the glazing can be adjusted. This is possible by colouring a fluid, which is circulated in chambers of the glazing. The concentration of the colorant can be infinitely adjusted. In addition, this window allows collecting heat in the exterior fluid and allows the interior fluid chamber to operate as heating panel. This paper presents a first assessment of different control strategies for adjusting the colorant concentration with a simplified model. The assessed control strategies result in considerably different overall energy demands. Certain control strategies have high potential for reducing the energy demand for heating and cooling depending on the locations (Munich 20–30% , Madrid 50–70% , Dubai 50–60%). However, certain control strategies increase the electricity demand for lighting, which needs to be considered in the further development. In general, control strategies that only consider the solar irradiation are less promising strategies in temperate climate than strategies that also take the interior temperature into account. The results of controls that also respect the thermal comfort based on a Predicted Mean Vote (PMV) index can achieve low energy demand, presuming that a deviation from the highest level of comfort is acceptable. At this stage of research, none of the studied control strategies shows to be optimal for all climate conditions to achieve highest energy reductions. Further research is necessary in the development of a control strategy that can universally be applied