Abstract
In 2013 83% of energy in the German residential sector is used for the preparation of domestic hot water (13%) and space heating (70%). Thermal demand profiles are essential to correctly determine operation and sizing of heating technologies. In this work, the stochastic bottom-up approach for electric loads is extended to cover domestic hot water (DHW) and space heating demands. The approach is presented for individual buildings and residential areas, validated and compared to currently used approaches. A behavioural model is used to determine DHW tappings, electric appliance use and temperature settings of the building. Building heat load is calculated using a simplified physical model, to allow for realistic energy demand profiles, efficient model parametrisation and fast computation. A randomisation approach for building heat load based on a clustered building typology, a variation of building parameters and heating settings is presented which allows the simulation of larger quantities of similar buildings. Validation against measured data for German single family houses shows a correlation of the typical daily load profile for DHW consumption of 0.92 and a mean relative error of 3% and for space heating 0.89 and 9% respectively.
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