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This study aims to develop a gridded optimization model for studying photovoltaic applications in Nordic countries. The model uses the spatial and temporal data generated by the mesoscale models STRÅNG and MESAN developed by the Swedish Meteorological and Hydrological Institute. The model is developed based on the comparison between five irradiance databases, three decomposition models, two transposition models, and two photovoltaic models. Several techno-economic and environmental aspects of photovoltaic systems and photovoltaic systems integrated with batteries are investigated from a spatial perspective. CM SAF SARAH-2, Engerer2, and Perez1990 have shown the best performances among the irradiance databases, and decomposition and transposition models, respectively. STRÅNG resulted in the second-best irradiance database to be used in Sweden for photovoltaic applications when comparing hourly global horizontal irradiance with weather station data. The developed model can be employed for carrying out further detailed gridded techno-economic assessments of photovoltaic applications and energy systems in general in Nordic countries. The model structure is generic and can be applied to every gridded climatological database worldwide.

About 35% of Europe’s building stock is over 50 years old and consumes about 175 kWh/m2 for heating, between 3-5 times the amount required by the newly constructed buildings. Annually, between1 and 1.5% new buildings are built and only between 0.2 and 0.5% are removed, therefore the focus needs to be put on the renovation of the existing building stock. The implementation of energy conservation measures (ECMs) in the residential sector becomes a very important strategy to meet the EU´s 20% energy consumption reduction of the 20-20-20 goals. The main challenge, however, is to determine which of the ECMs strategies are the best to provide not just with the best energy consumption reduction, but also with the best environmental impact and economic benefits. This paper addresses this issue and analyses the impact of different ECMs by focusing not only on the buildings themselves, but on the energy supply network and the overall energy system as a whole. To achieve this, we review five case studies in Sweden that use different ECMs as well as other alternatives, such as: distributed generation (DG) and energy storage. Results suggest that although there is no standard protocol that would fit all renovation projects, the existing methodologies fall short to provide the best overall impact on the energy system and that a broader analysis of the local conditions should be carried out before performing large building renovation projects.

This report covers evaluation of some renovation projects and compares energy saving effects versus renovation costs.

It can be seen that advanced renovation to passive house standard is significantly more expensive than “normal” renovation, but also gives significant improvement by a 62 % reduction of total energy and 85 % reduction in heat demand. The cost associated with the renovation is somewhere in the range of 130–570 €/m², depending on how the total renovation costs are split between energy and other aspects. Probably somewhere in-between is most correct. This can be compared to mostly better heat control by measuring temperature in every third apartment and controlling heat supply to keep a constant temperature. This gives the possibility to have a significantly lower set point, 21 ºC instead of 24 ºC as earlier. Together with some other actions, 34 % energy savings were achieved at a cost of 28 €/m². Also renovations with significantly more actions were evaluated, where the cost also is in-between.

From this we can conclude that with more advanced and costly renovations we can achieve very strong reductions, which may be feasible if the renovation demand is high anyhow, while cheap and low cost actions can be good enough for quite good buildings.

Also behavior with respect to energy use was evaluated. We here can see that the use is very different in different apartments depending on behavior. Energy information actions were giving positive effects on energy demand for the majority of investigated tenants, while approximately 25 % did not reduce or even increased their consumption.

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.

Increased energy performance of the building stock of European Union is seen as an important measure towards mitigating climate change, increasing resource utilisation efficiency and energy supply security. Whether to improve the supply-side, the demand-side or both is an open issue. This conflict is even more apparent in countries such as Sweden with a high penetration of district heating (DH). Many Swedish DH systems have high share of secondary energy resources such as forest industry residuals, waste material incineration and waste heat; and resource efficient cogeneration of electricity in combined heat and power (CHP) plants. When implementing an energy conservation measure (ECM) in a DH connected building stock, it will affect the operation of the whole DH system. If there are CHP plants and the cogeneration of electricity decreases due to an ECM, and this electricity is valued higher than the fuel savings, the consequences of the ECM would be negative. 

These complex relationships are investigated by conducting a case study on the Eskilstuna DH system, a renewable energy supply system with relatively high share of cogenerated electricity. Heat demand profiles of ECMs are determined by building energy simulation, using recently deep energy retrofitted multifamily buildings of the “Million Programme”-era in Eskilstuna as model basis. How implementing ECMs impact on the DH system’s heat and electricity production under different electricity revenue scenarios has been computed and evaluated in terms of resource efficiency and CO₂ emissions. 

The results show that different ECMs in the buildings impact differently on the DH system. Measures such as improved insulation level of the building’s envelope, that decrease the heat demand’s dependence to outdoor temperature, increase the amount of cogenerated electricity. While measures such as thermal solar panels, which save heat during summer, affects the absolute amount of cogenerated electricity negatively. Revenues from cogenerated electricity influence the amount of cost-effectively produced electricity much more than the impact from ECMs. Environmental benefits of the ECMs, measured in CO₂ emissions and primary energy consumption, are quite small in DH systems that have high share of forest residual fuels and electricity cogeneration. The consequences can even be negative if ECMs lead to increased need of imported electricity that is produced resource inefficiently or/and by fossil fuels. However, all studied ECMs increase the relative amount of cogenerated electricity, the ratio between amount of cogenerated electricity and the heat load. This implied that all ECMs increase the overall efficiency of the Eskilstuna DH system.

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.

There is a global need to reduce energy consumption and integrate a larger share of renewable energy production while meeting expectations for human well-being and economic growth. Buildings have a key role to play in this transition to more sustainable cities and communities.

Building energy modeling (BEM) and simulation are needed to gain detailed knowledge ofthe heat flows and parameters that determine the thermal energy performance of a building. Remote sensing techniques have enabled the generation of geometrical representations of existing buildings on the scale of entire cities. However, parameters describing the thermal properties ofthe building envelope and the technical systems are usually not readily accessible in a digitized form and need to be inferred. Further, buildings are complex systems with indoor environmental conditions that vary dynamically under the stochastic influence of weather and occupant behavior and the availability of metering data is often limited. Consequently, robust inference is needed to handle high and time-varying uncertainty and a varying degree of data availability.

This thesis starts with investigation of meteorological reanalyses, remote sensing and onsite metering data sources. Next, the developed dynamic and physics-based BEM, consisting of a thermal network and modeling procedures for the technical systems, passive heat gains and boundary conditions, is presented. Finally, the calibration framework is presented, including a method to transform a deterministic BEM into a fully probabilistic BEM, an iterated extended Kalman filtering algorithm and a probabilistic calibration procedure to infer uncertain parameters and incorporate prior knowledge.

The results suggest that the proposed BEM is sufficiently detailed to provide actionable insights, while remaining identifiable given a sufficiently informative prior model. Such a prior model can be obtained based solely on knowledge of the underlying physical properties of the parameters, but also enables incorporation of more specific information about the building. The probabilistic calibration approach has the capability to combine evidence from both data and knowledge-based sources; this is necessary for robust inference given the often highly uncertain reality in which buildings operate.

The contributions of this thesis bring us a step closer to producing models of existing buildings, on the scale of whole cities, that can simulate reality sufficiently well to gain actionable insights on thermal energy performance, enable buildings to act as active components of the energy system and ultimately increase the operational resilience of the built environment.

Dynamic building simulation is increasingly necessary for accurately quantifying potential energy savings measures in retrofit projects, to compliant with new stricter directives from EU implanted into member states legislations and building codes. For good result the simulation model need to be accurately calibrated. This requires actual weather data, representative for the climate surrounding the given building, in order to calibrate against actual energy bills of the same period of time.

The main objective of this degree project is to combine observed weather (temperature, humidity, wind etc.) data with modeled solar radiation data, utilizing the SMHI STRÅNG model system; and transform these data into AMY (Actual Meteorological Year) files to be used with building simulation software. This procedure gives actual weather datasets that will cover most of the urban and semi urban area in Northern Europe while still keeping the accuracy of observed weather data. A tool called Real-Time Weather Converter was developed to handle data retrieval & merging, filling of missing data points and to create the final AMY-file.

Modeled solar radiation data from STRÅNG had only been validated against a Swedish solar radiation network; validation was now made by the author with wider geographic coverage. Validation results show that STRÅNG model system performs well for Sweden but less so outside of Sweden. There exist some areas outside of Sweden (mainly Central Europe) with reasonable good result for some periods but the result is not as consistent in the long run as for Sweden.

The missing data fill scheme developed for the Real-Time Weather Converter does perform better than interpolation for data gaps (outdoor temperature) of about 9 to 48 hours. For gaps between 2 and 5 days the fill scheme will still give slightly better result than linear interpolation. Akima Spline interpolation performs better than linear interpolation for data gaps (outdoor temperature) in the interval 2 to about 8 hours.

Temperature uncertainty was studied using data from the period 1981-2010 for selected sites. The result expressed as SD (Standard Deviation) for the uncertainty in yearly mean temperature is about 1˚C for the Nordic countries. On a monthly basis the variation in mean temperature is much stronger (for Nordic countries it ranges from 3.5 to 4.7 ˚C for winter months), while summer months have less variation (with SD in the range of 1.3 to 1.9 ˚C). The same pattern is visible in sites at more southern latitudes but with much lower variation, and still lower for sites near coast areas. E.g. the cost-near Camborne, UK, has a SD of 0.7 to 1.7 ˚C on monthly basis and yearly SD of 0.5 ˚C.

Mean direct irradiance SD for studied sites ranges from 5 to 19 W/m² on yearly basis, while on monthly basis the SD ranges from 40 to 60 W/m² for summer months. However, the sample base was small and of inconsistent time periods and the numbers can only be seen as indicative.

The commonly used IWEC (International Weather for Energy Calculations) files direct radiation parameter was found to have a very strong negative bias of about 20 to 40 % for Northern Europe.  These files should be used with care, especially if solar radiation has a significant impact of on the building being modeled. Note that there exist also a newer set of files called IWEC2 that can be purchased from ASHRAE, these files seems not to be systematically biased for North Europe but haven’t been studied in this paper.

The STRÅNG model system does catch the trend, also outside of Sweden, and is thus a very useful source of solar radiation data for model calibration.

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. 

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.

The 100 largest Swedish district heating (DH)-networks were studied on how DH conservation measures impacts CO2-emission rates taking both direct and indirect (i.e. displaced electricity) emissions into account, applying six different methods for the indirect emissions assessment. When the marginal electricity approach is applied on low CO2-emitting DH-networks with a high share of cogenerated electricity, it resulted in assessments that imply that DH conservation leads to higher CO2 emissions. This was not the case with the efficiency method.

When housing companies plan for energy conserving renovations, costs and amount of saved energy are usually estimated with yearly mean values. Yet the fuel mix varies widely depending on heat demand of district heating system, often with higher cost and CO2 emission rates during winter than summer.

Instead of comparing different energy conserving measures’ potentials with yearly mean values, it would be beneficial to examine them in a higher resolution, e.g. on daily or monthly basis, to identify real effectiveness of different measures in reducing CO2-emissions and primary energy consumption.

In this study, three energy conserving measures are put into a building simulation model to obtain results of hourly energy consumption reduction, which is then fitted into a district heating optimization model to analyze the impact on district heating system.

This study also discuss the correlation between energy cost for the customer and different measures’ environmental impact under new circumstances: seasonal energy price models of district heating, a price model which introduce price fluctuation throughout a year. This new factor provides a more comprehensive incitement to the property owners to encourage them to make environmental friendly decisions when planning for energy conserving renovations.

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.