• Energy Research

With a global upsurge in energy demands, the challenge to improve the energetic performance of existing buildings calls out for district and building level interventions through precise energy demand estimations, retrofitting measures and the recognition of energy efficient solutions within the built environment. Often the amount and type of data required for district-level energy calculations are unclear, and it leaves decision-makers in a dilemma within the development of low to high quality data sources based on its value as perceived by stakeholders. There exists a lack of robust indicators that could guide urban planners and policy makers to channelise their resources in reducing energy consumption based on the input data for district level energy simulation. Keeping in mind the inadequacy of coherent computational models and informing the users about the implications of acquiring different input data, it is vital to develop methodologies to optimally model, store and exchange city-wide physical, operational, environmental, geometrical and contextual data. Urban Building Energy Modelling (UBEM), a technique to model urban areas quantitatively helps define georeferenced building-related data and energy-specific attributes to compute and identify a building’s energy performance characteristics. UBEM assists the quantification of a building’s energy demand, consumption, and the effects it possesses onto its surrounding buildings’ energy requirements. The technique enables virtual modelling of the built environment and allows representing individual buildings in the form of data models. These models provide a standardised structure for storage and exchange of building-specific data and allow its extension with energy-specific attributes for energy simulations. One such model, the City Geographical Markup Language (CityGML) is prevalent within the UBEM community and is employed for applications related to energy. Although the model has an extensive usage, its limited availability, inconsistency, complexity, and reproducibility are always questioned upon. Therefore, this thesis highlights the strengths and shortcomings of CityGML and proposes novel solutions to overcome the identified limitations in using the format for UBEM. This thesis utilises a UBEM-based implementation methodology and introduces methods to assist stakeholders in individual stages of an urban-scale energy simulation process. The proposed methods facilitate the generation and deployment of CityGML building models using raw input data, and provide techniques to enrich the models with energy-specific attributes that are required for heating energy demand simulation. The developed methods, consolidated as DESCity, envision to increase CityGML’s usage in UBEM by allowing users of varying experience levels to use the methods semi-automatically. The incorporated mathematical computations and the developed process algorithms allow users to model, validate, transform, enrich, and simulate CityGML building models without the need of explicit modelling and in an uncomplicated manner. The proposed methodology for DESCity provides functionalities to analyse, search and validate existing CityGML building models incorporated within the CityGML Analysis Toolbox (CityATB). The methodology further includes CityGML Building Interpolation Tool (CityBIT) that helps generate new building models by using user-defined inputs and interpolation techniques. The functions incorporated within the DESCity’s CityGML LoD Transformation Tool (CityLDT) enable the trans formation of a CityGML model’s granularity as the implementation methodology enables users to upscale or downscale the CityGML Levels of Detail (LoD). The developed workflow of CityGTV facilitates geometric transformation and validation of building models as required by its users. For enhancing the geometric data models with energy-specific attributes, the methodology of DESCity includes CityGML Enrichment Tool (CityEnrich) and TEASER+ that enable manual and archetype-based enrichment, respectively. Apart from enriching the building models with energy-related data, both the implemented workflows enable the export of an enriched CityGML model in the form of Energy Application Domain Extension (Energy ADE). Each workflow within DESCity has its own user interface and the functionalities of the platform are available as open source. This thesis highlights the adapted methodology and implementation of the aforementioned functions and demonstrates the applicability of DESCity’s modules in urban-scale heating energy demand simulations. It further reflects upon the current limitations of the development and conclusively discusses the potential future research direction within the field. Urban planners, policy makers and engineers could use the developed methodology to model, enrich and simulate building models for determining and identifying the heating energy demands and energy-efficiency potential, particularly, at an urban scale. The devised approach reduces the necessity of explicit modelling of the city-wide buildings, and enables stakeholders to generate, validate, analyse and simulate 3D building models in CityGML. The work at hand could be used to develop digital representations of the urban buildings with limited data and resources, and the developed methodology could be integrated in existing workflows and software for supporting data exchange and reproducibility. Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2023; Aachen : RWTH Aachen University 1 Online-Ressource : Illustrationen, Diagrammme (2023). = Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2023 Published by RWTH Aachen University, Aachen

Urban densification increases the number of people living in urban areas and is hypothesized to be a more efficient use of available land than urban sprawl. The objective of this study was to quantify the operational and embodied emissions created as a result of densification. A ‘Business as usual’ and a ‘Concentrated’ densification strategy were investigated. When densifying at the neighbourhood level, existing buildings can either be replaced or extended to accommodate the additional inhabitants. The densification strategies were applied to two reference urban design neighbourhoods in Switzerland. The ‘Typical’ approach assumed that all the buildings were demolished and rebuilt and the ‘Preserve-existing’ approach involved the extension of existing buildings as much as possible. Construction material choice and modification of the built form were the sources of embodied emissions considered for each strategy. Urban building energy modelling was used to calculate the emissions incurred by heating the buildings and the embodied emissions were calculated using building standards. The operational performance was simulated assuming both a gas boiler and an electric heat pump to determine the influence of the heating system type on the operational emissions. This study found that savings of approximately 30% in embodied emissions can be achieved by extending the existing building stock rather than rebuilding. However, these savings represent a relatively small percentage of the total emissions incurred throughout a building's lifetime and the savings further diminish in the concentrated densification strategies. Energy and Buildings, 276 ISSN:1872-6178 ISSN:0378-7788

This project aims to contribute to the energy mapping of Visby’s building stock. Using energy performance certificates data, the building stock can be categorized after defining energy related parameters. From every category, an archetype building is developed from the mean value of interesting parameters in each group. The goal is to create distinct categories which as well as possible represents the building stock. This study commences with an inventory of Visby’s building stock to create an overall picture. Thereafter categories of the building stock are examined on the basis of interesting parameters that supposedly have an impact on the building’s energy consumption. The purpose is to examine which parameters that together catches the most representative picture of Visby’s building stock. The energy performance of the buildings is the main factor for comparison. Parameters that are used in the categorization process are: Building type, location of the buildings, building volume, type code, year of construction, heating system and number of floors. From the determined categorizations, archetypes are developed and type buildings can be represented. This study only examines residential buildings. Two times during the study, abnormal values were removed from the used material. The cleaning of abnormal values prevents that a few values which are considered as abnormal gives a wrong picture of the building stocks characteristics. The first analysis of the parameters leads to 6 examined categorizations where two are selected, one for single family housing and one for multi-family housing. Parameters that best defines single family housing are year of construction, heating system and building volume. Parameters that best defines multi-family housing are year of construction and building volume. The established categorization covers 92% of the building stock and 205 buildings are erased by the purge. The standard deviation on the mean value of energy performance has the average value of 30 kWh/m2 in the categorization for single family housing. For multi-family housing the standard deviation of the mean value is on average 17 kWh/m2 in the categorization. The high value of standard deviation for single family housing is affected by the wide range of energy performances values. These values could be caused by buildings that have had renovations or that other parameters that has not been taken into consideration in this study could have been better in the categorization. Detta projekt syftar att bidra till energikartläggningen av Visbys byggnadsbestånd. Med hjälp av energideklarationsdata kategoriseras byggnadsbeståndet efter definierande energirelaterade parametrar. Ur varje kategori tas en arketypbyggnad fram från medelvärdet av intressanta parametrar inom sin grupp. Målet är att skapa distinkta arketyper som fångar en så stor del av byggnadsbeståndet som möjligt. Studien inleds med en inventering av Visbys byggnadsbestånd för att skapa en helhetsbild, detta stadie benämns i rapporten som lägesöversikt. Därefter undersöks olika kategoriseringar av byggnadsbeståndet utifrån några intressanta parametrar som antas ha inverkan på byggnadernas energiförbrukning. Avsikten är att undersöka vilka parametrar som tillsammans fångar den mest representativa bilden av Visbys byggnadsbestånd. Byggnadernas energiprestanda utgör den huvudsakliga faktorn för jämförelse. Parametrar som undersöks i kategoriseringarna är: Byggnadstyp, läge på byggnaderna, byggnadsvolym, typkod, nybyggnadsår, uppvärmningssystem och antal plan. Ur fastställda kategorier tas sedan arketyper och typbyggnader fram. Studien avgränsas till att endast undersöka bostadsbyggnader. Det använda materialet rensas på abnorma värden vid två tillfällen under studiens gång. Rensningen förhindrar att ett fåtal utstickande värden ger en missvisande bild av byggnadsbeståndets egenskaper. Lägesöversikten leder till 6 undersökta kategoriseringar där två väljs ut: en för flerbostadshus och en för småhus. Parametrar som bäst definierar gruppen småhus är nybyggnadsår, uppvärmningssystem och byggnadsvolym. Parametrar som bäst definierar gruppen flerbostadshus är nybyggnadsår och byggnadsvolym. Den fastställda kategoriseringen täcker 92% av byggnadsbeståndet och totalt stryks 205 byggnader vid rensning. Standardavvikelsen i energiprestanda ligger i snitt på 30 kWh/m2 i kategoriseringen för småhus och i snitt på 17 kWh/m2 i kategoriseringen för flerbostadshus. Den stora spridningen av energiprestanda i gruppen småhus kan till exempel bero på att vissa byggnader blivit renoverade eller att parametrar som bättre definierar gruppen inte har undersökts i denna studie.

This project aims to contribute to the energy mapping of Visby’s building stock. Using energy performance certificates data, the building stock can be categorized after defining energy related parameters. From every category, an archetype building is developed from the mean value of interesting parameters in each group. The goal is to create distinct categories which as well as possible represents the building stock. This study commences with an inventory of Visby’s building stock to create an overall picture. Thereafter categories of the building stock are examined on the basis of interesting parameters that supposedly have an impact on the building’s energy consumption. The purpose is to examine which parameters that together catches the most representative picture of Visby’s building stock. The energy performance of the buildings is the main factor for comparison. Parameters that are used in the categorization process are: Building type, location of the buildings, building volume, type code, year of construction, heating system and number of floors. From the determined categorizations, archetypes are developed and type buildings can be represented. This study only examines residential buildings. Two times during the study, abnormal values were removed from the used material. The cleaning of abnormal values prevents that a few values which are considered as abnormal gives a wrong picture of the building stocks characteristics. The first analysis of the parameters leads to 6 examined categorizations where two are selected, one for single family housing and one for multi-family housing. Parameters that best defines single family housing are year of construction, heating system and building volume. Parameters that best defines multi-family housing are year of construction and building volume. The established categorization covers 92% of the building stock and 205 buildings are erased by the purge. The standard deviation on the mean value of energy performance has the average value of 30 kWh/m2 in the categorization for single family housing. For multi-family housing the standard deviation of the mean value is on average 17 kWh/m2 in the categorization. The high value of standard deviation for single family housing is affected by the wide range of energy performances values. These values could be caused by buildings that have had renovations or that other parameters that has not been taken into consideration in this study could have been better in the categorization.  Detta projekt syftar att bidra till energikartläggningen av Visbys byggnadsbestånd. Med hjälp av energideklarationsdata kategoriseras byggnadsbeståndet efter definierande energirelaterade parametrar. Ur varje kategori tas en arketypbyggnad fram från medelvärdet av intressanta parametrar inom sin grupp. Målet är att skapa distinkta arketyper som fångar en så stor del av byggnadsbeståndet som möjligt. Studien inleds med en inventering av Visbys byggnadsbestånd för att skapa en helhetsbild, detta stadie benämns i rapporten som lägesöversikt. Därefter undersöks olika kategoriseringar av byggnadsbeståndet utifrån några intressanta parametrar som antas ha inverkan på byggnadernas energiförbrukning. Avsikten är att undersöka vilka parametrar som tillsammans fångar den mest representativa bilden av Visbys byggnadsbestånd. Byggnadernas energiprestanda utgör den huvudsakliga faktorn för jämförelse. Parametrar som undersöks i kategoriseringarna är: Byggnadstyp, läge på byggnaderna, byggnadsvolym, typkod, nybyggnadsår, uppvärmningssystem och antal plan. Ur fastställda kategorier tas sedan arketyper och typbyggnader fram. Studien avgränsas till att endast undersöka bostadsbyggnader. Det använda materialet rensas på abnorma värden vid två tillfällen under studiens gång. Rensningen förhindrar att ett fåtal utstickande värden ger en missvisande bild av byggnadsbeståndets egenskaper. Lägesöversikten leder till 6 undersökta kategoriseringar där två väljs ut: en för flerbostadshus och en för småhus. Parametrar som bäst definierar gruppen småhus är nybyggnadsår, uppvärmningssystem och byggnadsvolym. Parametrar som bäst definierar gruppen flerbostadshus är nybyggnadsår och byggnadsvolym. Den fastställda kategoriseringen täcker 92% av byggnadsbeståndet och totalt stryks 205 byggnader vid rensning. Standardavvikelsen i energiprestanda ligger i snitt på 30 kWh/m2 i kategoriseringen för småhus och i snitt på 17 kWh/m2 i kategoriseringen för flerbostadshus. Den stora spridningen av energiprestanda i gruppen småhus kan till exempel bero på att vissa byggnader blivit renoverade eller att parametrar som bättre definierar gruppen inte har undersökts i denna studie.

In this master thesis, a methodology is proposed for building stock classification and archetype building development based on deterministic information available in Energy Performance Certificates (EPCs) of existing buildings in the city of Uppsala.This study aims to answer if the EPC database can be used as a reliable data source for archetype development and further UBEM models.The EPC data is cleaned and organised using Matlab. The building stock is then categorised into archetypes by energy performance and building characteristics and a model of each archetype building is created in the software EnergyPlus.The South-West part of Uppsala is used as a case study and to represent the building stock of that area 20 archetypes is developed. Simulations in EnergyPlus shows that the defined archetypes is a reliable estimation of buildings in Sweden with the same characteristics and construction period.By using GIS data the results can be aggregated to city level with the resulting total energy demand for heating calculated to 1455,7 GWh, compared to the actual value of 1397,0 GWh.The lack of validation data on a smaller scale is a large issue for this study, as well as some issues with data reliability in the EPCs. Despite this, the results of this study points to that the gathered values are a decent enough estimate to make a reliable assumption of the total energy demand for heating. The EPCs thus provide a useful source of data for energy demand purposes and building characteristics.

Cities are currently responsible for an important part of energy consumption and greenhouse gas emissions, justifying the need to develop measures to help them become more sustainable. One of those measures can be to address under-utilized assets in cities, such as derelict buildings with high potential for rehabilitation, and the establishment of new residence hubs within cities. Consequently, this work establishes a novel framework for evaluating the impact of rehabilitating these buildings in an urban area in Lisbon, considering the energy consumption associated with the usage of the dwelling as well as the impact on mobility, since it was considered that these buildings will be occupied by people who currently work nearby but live in the outskirts of Lisbon, favouring an urban planning of proximity between home and work. To this extent, a methodology was developed for selecting the buildings to be analysed and the commuting movements to be replaced. Then, buildings were simulated in an urban building energy modelling (UBEM) tool, considering three rehabilitation scenarios, and the required primary energy, CO2 emissions, and costs were calculated. Regarding mobility, three new scenarios were compared with the current scenario. The results obtained confirm the high potential savings from the rehabilitation of derelict buildings and in the best-case scenario—corresponding to the rehabilitation considering envelope insulation, the installation of efficient windows, and the adoption of a heat pump together with a mobility standard targeting 15 min cities—reductions of 76% in primary energy and 84% in CO2 emissions were achieved.

Building simulation supports the conceptual design, planning and operation of innovative and economical energy supply options for buildings and cities. Typical applications of Urban Building Energy Modeling are quantification of the impact of retrofits or the optimization of heat transfer of district heating networks. The necessary input data for design-driven building simulation are often not available for existing buildings. Archetypes are used to fill the missing information and thus enable a dynamic simulation of a large number of existing buildings. The simulation results of archetypes show differences to hourly measured data of individual buildings due to statistical and discrete assumptions. To improve the description and prediction of individual buildings, the calibration of simulation models is key. This thesis describes a method for automated calibration of individual urban building simulation models using hourly measurement data. The proposed framework automates all necessary steps from model generation, selection of sensitive parameters, calibration, and evaluation using statistical indices. The model generation uses the standardized information model CityGML and its extension EnergyADE and utilizes all available building data. In addition, further building parameters are identified with the help of the analysis of existing measurement data. The selection of sensitive parameters is done for each building and thus takes into account specific building characteristics. Bayesian calibration is used as the calibration method, which is particularly characterized by the determination of probability distributions. Hourly measurements are considered in calibration and evaluation of the simulation results with a combination of several statistical indices. The application of the framework to emulated and real buildings helps to better understand the strengths and weaknesses of automated calibrations for building models. It was shown that the reliability of automated calibration cannot be evaluated based on simulation results alone, but the calibrated parameter values have to be taken into account. In this context, compensation effects were described in detail. It could be shown that the more information is used for the parameterization of the initial building model and the more measurement data is available in hourly resolution, the better is the calibration of the models in comparison to the simulation results and the lower are compensation effects.