Building energy performance modeling is essential for energy planning, management, and efficiency. This paper presents a space heating model suitable for auto-generating baseline models of existing multifamily buildings. Required data and parameter input are kept within such a level of detail that baseline models can be auto-generated from, and calibrated by, publicly accessible data sources. The proposed modeling framework consists of a thermal network, a typical hydronic radiator heating system, a simulation procedure, and data handling procedures. The thermal network is a lumped and simplified version of the ISO 52016-1:2017 standard. The data handling consists of procedures to acquire and make use of satellite-based solar radiation data, meteorological reanalysis data (air temperature, ground temperature, wind, albedo, and thermal radiation), and pre-processing procedures of boundary conditions to account for impact from shading objects, window blinds, wind- and stack-driven air leakage, and variable exterior surface heat transfer coefficients. The proposed model was compared with simulations conducted with the detailed building energy simulation software IDA ICE. The results show that the proposed model is able to accurately reproduce hourly energy use for space heating, indoor temperature, and operative temperature patterns obtained from the IDA ICE simulations. Thus, the proposed model can be expected to be able to model space heating, provided by hydronic heating systems, of existing buildings to a similar degree of confidence as established simulation software. Compared to IDA ICE, the developed model required one-thousandth of computation time for a full-year simulation of building model consisting of a single thermal zone. The fast computation time enables the use of the developed model for computation time sensitive applications, such as Monte-Carlo-based calibration methods.

10This study highlights the forthcoming problem with diminishing environmental benefits from heat demand reducing 11 energy conservation measures (ECM) of buildings within district heating systems (DHS), as the supply side is 12 becoming "greener" and more primary energy efficient. In this study heat demand profiles and annual electricity-to-heat 13 factors of ECMs in buildings are computed and their impact on system efficiency and greenhouse gas emissions of a 14Swedish biomass fuelled and combined heat and power utilising DHS are assessed. A weather normalising method for 15 the DHS heat load is developed, combining segmented multivariable linear regressions with typical meteorological year 16 weather data to enable the DHS model and the buildings model to work under the same weather conditions. Improving 17 the buildings' envelope insulation level and thereby levelling out the DHS heat load curve reduces greenhouse gas 18 emissions and improves primary energy efficiency. Reducing household electricity use proves to be highly beneficial, 19 partly because it increases heat demand, allowing for more cogeneration of electricity. However the other ECMs 20 considered may cause increased greenhouse gas emissions, mainly because of their adverse impact on the cogeneration 21 of electricity. If biomass fuels are considered as residuals, and thus assigned low primary energy factors, primary 22

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