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

Abstract

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.