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

This work presents a method to make use of gridded historical mesoscale datasets for energy and hygrothermal building modelling and simulation purposes by transforming, merging and formatting them into time series. The main result of this work is a web tool, https://rokka.shinyapps.io/shinyweatherdata, allowing users to create actual climate dataset for any location in North Europe in file formats used by common building simulations tools. A review is conducted on freely available gridded mesoscale datasets/model systems for north Europe: the modelling systems MESAN and STRÅNG currently used as data source for the developed web tool as well as the SARAH model system and MESAN/MESCAN reanalysis datasets.

A framework for adaptive weather correction of energy consumption data is presented. The procedure is conducted in two steps: I) a regression model is trained on a building's recent historical energy consumption, weather and calendar data; II) energy consumption is predicted by using long term weather data as input to the trained model. Thus the buildings long term energy consumption is obtained, from which the building's expected (alias normalised or weather corrected) yearly energy consumption is derived. For older Swedish residential buildings, the proposed regression method matches traditional heating degree days method in accuracy. But for low energy and near zero energy buildings the regression method is more accurate, especially for years of extreme weather and for building with more complex installations such as heat pumps or solar thermal panels. The main benefit of the developed weather correction method is that it adapts to the data, therefore most buildings (or any kinds of weather dependent processes) can be weather corrected in an automated way. © 2017 The Authors. Published by Elsevier Ltd.

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. 

Hourly energy readings from heat billing meters are valuable data source for the energy performance assessment of district heating substations and the buildings they serve. The quality of such analyses is bounded by the accuracy of the hourly readings. Thus, assessing the accuracy of the hourly heat meter readings is a necessary (but often overlooked) first step to ensure qualitative subsequent analyses. Due to often limited bandwidth capacity hourly readings are quantized before transmission, which can cause severe information loss. In this paper, we study 266 Swedish heat meters and assess the quantization effect by information entropy ranking. Further, a detailed comparison is conducted with three heat meters with typically occurring quantization errors. Uncertainty due to the quantization effect is compared with the uncertainty due to typical accuracy of the meter instrumentation. A method to conflate information from both energy readings and energy calculated from flow and temperature readings is developed. The developed conflation method is shown to be able to decrease uncertainty for heat meters with severely quantized energy readings. However, it is concluded that a preferable approach is to work with the heat meter infrastructure to ensure the future recorded readings holds high enough quality to be useful for energy performance assessments with hourly or sub-hourly readings.

Reliable energy models are needed to determine building energy performance. Relatively detailed energy models can be auto-generated based on 3D shape representations of existing buildings. However, parameters describing thermal performance of the building fabric, the technical systems, and occupant behavior are usually not readily available. Calibration with on-site measurements is needed to obtain reliable energy models that can offer insight into buildings' actual energy performances. Here, we present an energy model that is suitable for district-heated multifamily buildings, based on a 14-node thermal network implementation of the ISO 52016-1:2017 standard. To better account for modeling approximations and noisy inputs, the model is converted to a stochastic state-space model and augmented with four additional disturbance state variables. Uncertainty models are developed for the inputs solar heat gains, internal heat gains, and domestic hot water use. An iterated extended Kalman filtering algorithm is employed to enable nonlinear state estimation. A Bayesian calibration procedure is employed to enable assessment of parameter uncertainty and incorporation of regulating prior knowledge. A case study is presented to evaluate the performance of the developed framework: parameter estimation with both dynamic Hamiltonian Monte Carlo sampling and penalized maximum likelihood estimation, the behavior of the filtering algorithm, the impact of different commonly occurring data sources for domestic hot water use, and the impact of indoor air temperature readings.