Modelling Urban Housing Stocks for Building Energy Simulation using CityGML EnergyADE

Understanding the energy demand of a city’s housing stock is an important focus for local and national administrations to identify strategies for reducing carbon emissions. Building energy simulation offers a promising approach to understand energy use and test plans to improve the efficiency of residential properties. As part of this, models of the urban stock must be created that accurately reflect its size, shape and composition. However, substantial effort is required in order to generate detailed urban scenes with the appropriate level of attribution suitable for spatially explicit simulation of large areas. Furthermore, the computational complexity of microsimulation of building energy necessitates consideration of approaches that reduce this processing overhead. We present a workflow to automatically generate 2.5D urban scenes for residential building energy simulation from UK mapping datasets. We describe modelling the geometry, the assignment of energy characteristics based upon a statistical model and adopt the CityGML EnergyADE schema which forms an important new and open standard for defining energy model information at the city-scale. We then demonstrate use of the resulting urban scenes for estimating heating demand using a spatially explicit building energy microsimulation tool, called CitySim+, and evaluate the effects of an off-the-shelf geometric simplification routine to reduce simulation computational complexity

Using unsupervised learning to partition 3D city scenes for distributed building energy microsimulation

Microsimulation is a class of Urban Building Energy Modeling techniques in which energetic interactions between buildings are explicitly resolved. Examples include SUNtool and CitySim+, both of which employ a sophisticated radiosity-based algorithm to solve for radiation exchange. The computational cost of this algorithm increases in proportion to the square of the number of surfaces of which an urban scene is comprised. To simulate large scenes, of the order of 10,000 to 1,000,000 surfaces, it is desirable to divide the scene to distribute the simulation task. However, this partitioning is not trivial as the energy-related interactions create uneven inter-dependencies between computing nodes. To this end, we describe in this paper two approaches (K-means and Greedy Community Detection algorithms) for partitioning urban scenes, and subsequently performing building energy microsimulation using CitySim+ on a distributed memory High-Performance Computing Cluster. To compare the performance of these partitioning techniques, we propose two measures evaluating the extent to which the obtained clusters exploit data locality. We show that our approach using Greedy Community Detection performs well in terms of exploiting data locality and reducing inter-dependencies among sub-scenes, but at the expense of a higher data preparation cost and algorithm run-time

Scale aware modeling and monitoring of the urban energy chain

With energy modeling at different complexity levels for smart cities and the concurrent data availability revolution from connected devices, a steady surge in demand for spatial knowledge has been observed in the energy sector. This transformation occurs in population centers focused on efficient energy use and quality of life. Energy-related services play an essential role in this mix, as they facilitate or interact with all other city services. This trend is primarily driven by the current age of the Ger.: Energiewende or energy transition, a worldwide push towards renewable energy sources, increased energy use efficiency, and local energy production that requires precise estimates of local energy demand and production. This shift in the energy market occurs as the world becomes aware of human-induced climate change, to which the building stock has a significant contribution (40% in the European Union). At the current rate of refurbishment and building replacement, of the buildings existing in 2050 in the European Union, 75% would not be classified as energy-efficient. That means that substantial structural change in the built environment and the energy chain is required to achieve EU-wide goals concerning environmental and energy policy. These objectives provide strong motivation for this thesis work and are generally made possible by energy monitoring and modeling activities that estimate the urban energy needs and quantify the impact of refurbishment measures. To this end, a modeling library called aEneAs was developed in the scope of this thesis that can perform city-wide building energy modeling. The library performs its tasks at the level of a single building and was a first in its field, using standardized spatial energy data structures that allow for portability from one city to another. For data input, extensive use was made of digital twins provided from CAD, BIM, GIS, architectural models, and a plethora of energy data sources. The library first quantifies primary thermal energy demand and then the impact of refurbishment measures. Lastly, it estimates the potential of renewable energy production from solar radiation. aEneAs also includes network modeling components that consider energy distribution in the given context, showing a path toward data modeling and simulation required for distributed energy production at the neighborhood and district level. In order to validate modeling activities in solar radiation and green façade and roof installations, six spatial models were coupled with sensor installations. These digital twins are included in three experiments that highlight this monitoring side of the energy chain and portray energy-related use cases that utilize the spatially enabled web services SOS-SES-WNS, SensorThingsAPI, and FIWARE. To this author\u27s knowledge, this is the first work that surveys the capabilities of these three solutions in a unifying context, each having its specific design mindset. The modeling and monitoring activity and their corresponding literature review indicated gaps in scientific knowledge concerning data science in urban energy modeling. First, a lack of standardization regarding the spatial scales at which data is stored and used in urban energy modeling was observed. In order to identify the appropriate spatial levels for modeling and data aggregation, scale is explored in-depth in the given context and defined as a byproduct of resolution and extent, with ranges provided for both parameters. To that end, a survey of the encountered spatial scales and actors in six different geographical and cultural settings was performed. The information from this survey was used to put forth a standardized spatial scales definition and create a scale-dependent ontology for use in urban energy modeling. The ontology also provides spatially enabled persistent identifiers that resolve issues encountered with object relationships in modeling for inheritance, dependency, and association. The same survey also reveals two significant issues with data in urban energy modeling. These are data consistency across spatial scales and urban fabric contiguity. The impact of these issues and different solutions such as data generalization are explored in the thesis. Further advancement of scientific knowledge is provided specifically with spatial standards and spatial data infrastructure in urban energy modeling. A review of use cases in the urban energy chain and a taxonomy of the standards were carried out. These provide fundamental input for another piece of this thesis: inclusive software architecture methods that promote data integration and allow for external connectivity to modern and legacy systems. In order to reduce time-costly extraction, transformation, and load processes, databases and web services to ferry data to and from separate data sources were used. As a result, the spatial models become central linking elements of the different types of energy-related data in a novel perspective that differs from the traditional one, where spatial data tends to be non-interoperable / not linked with other data types. These distinct data fusion approaches provide flexibility in an energy chain environment with inconsistent data structures and software. Furthermore, the knowledge gathered from the experiments presented in this thesis is provided as a synopsis of good practices

Automatische Simulation von Wärmebedarf und -versorgung auf Quartiersebene

Die Bedeutung von Klimaschutz und Nachhaltigkeit sowie die Forderung nach konkreten Umsetzungsmaßnahmen nimmt in den letzten Jahren in Deutschland stetig zu. In den zuständigen Gemeinden und Landkreisen fehlen allerdings oftmals geeignete Tools zur Berechnung und Bewertung von individuell angepassten Szenarien zur Umstellung der Wärmeversorgung der Gebäude. Viele Analysen finden bislang top-down auf nationaler Ebene statt oder betrachten bottom-up Einzelgebäude. Die allerdings für die Entscheidung und Umsetzung relevante Zwischenebene von Quartieren wird nur selten adressiert. Daher wurde im Rahmen der vorliegenden Arbeit ein Verfahren entwickelt, welches es ermöglicht, Energiebilanzen sowie verschiedene Szenarien für eine nachhaltige Wärmeversorgung der Gebäude für ein Quartier oder eine ganze Kommune zu berechnen, bewerten und vergleichen. Es basiert auf Tools zur Modellierung und Simulation, die unabhängig von dieser Arbeit bereits die Bestimmung der lokalen erneuerbaren Potenziale sowie bestimmte bedarfsseitige Analysen umfassen und an der HFT Stuttgart seit 2013 entwickelt werden. Darauf aufbauend kommen im Rahmen der vorliegenden Arbeit verschiedene Methoden unter anderem aus dem Bereich der Geoinformatik, der heuristischen Entscheidungsfindung, der objektorientierten Modellierung mit UML sowie der mathematischen Modellbildung in Form der physikalische Modellierung mit Python, Java und INSEL zum Einsatz. Fokus ist die Verknüpfung der einzelnen Methoden um eine automatisierte Berechnung von zentralen und dezentralen Wärmeversorgungssystemen zu ermöglichen. Die Anwendung der entwickelten Methoden wird anhand verschiedener Fallstudien gezeigt. Dabei werden die einfache Anwendbarkeit sowie die Übertragbarkeit des Verfahrens demonstriert. Der Abgleich der Simulationsergebnisse mit Messdaten zeigt dabei eine nur geringe Abweichung von 6 % im Jahresmittel. Insgesamt steht mit den Erweiterungen dieser Arbeit ein Werkzeug zur Verfügung, welches den gesamten Bereich der urbanen Gebäudeenergiesimulation abdeckt. Mit nur wenigen, flächendeckend verfügbaren Informationen und einer anschaulichen Nutzeroberfläche können damit Szenarien für die erneuerbare Wärmeversorgung abgebildet und verglichen werden. Diese können zur Entscheidungsfindung in Kommunen und Landkreisen genutzt werden, um so die Umsetzung von konkreten Maßnahmen zur Reduktion von Treibhausgasemissionen zu beschleunigen

Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland

Building energy-use models and tools can simulate and represent the distribution of energy consumption of buildings located in an urban area. The aim of these models is to simulate the energy performance of buildings at multiple temporal and spatial scales, taking into account both the building shape and the surrounding urban context. This paper investigates existing models by simulating the hourly space heating consumption of residential buildings in an urban environment. Existing bottom-up urban-energy models were applied to the city of Fribourg in order to evaluate the accuracy and flexibility of energy simulations. Two common energy-use models—a machine learning model and a GIS-based engineering model—were compared and evaluated against anonymized monitoring data. The study shows that the simulations were quite precise with an annual mean absolute percentage error of 12.8 and 19.3% for the machine learning and the GIS-based engineering model, respectively, on residential buildings built in different periods of construction. Moreover, a sensitivity analysis using the Morris method was carried out on the GIS-based engineering model in order to assess the impact of input variables on space heating consumption and to identify possible optimization opportunities of the existing model

Modelling and Assessment of Biomass-PV Tradeoff within the Framework of the Food-Energy-Water Nexus

Food, water and energy are three essential resources for human well-being, poverty reduction and sustainable development. These resources are very much linked to one another, meaning that the actions in any one particular area often can have effects in one or both of the other areas. At the same time, an economy's shift towards climate neutrality requires a massive expansion of energy production from renewable sources. Among these ground-mounted photovoltaic (PV) and biomass will be expanded massively to meet the clean energy generation goal, simultaneously influence regional water and food availability and supply security. It is crucial to understand Food-Water-Energy Nexus (FWE) nexus during the energy transition. However, current studies have limitation both methodically (qualitative assessments) and spatially (aggregated data on a national level is more available). Firstly, a consistent share input data set in geographical format was created with the resolution of building/field. An energy simulation platform (SimStadt) was then extended with new workflows on biomass potential, ground-mounted PV potential, food demand/potential, and urban water demand. Combining with existing workflows on urban building heating/electricity demand and roof PV potential, the dissertation created a complete simulation environmental covering most-relating FWE topics in energy transition with consistent input and output structures at a fine resolution. Secondly, the most representative inter-linkage between ground-mounted PV and biomass on hinterland is investigated in details with the new tools. The output data of each field from ground-mounted PV and biomass workflows are linked and ranked according to the scenarios emphasizing PV yield, feasibility, profit, or biomass. The assessment and scenarios are applied at three representative German counties with distinguished land-use structures and geometries as case studies. Results show that current policies does not guarantee the technically efficient allocation of fields. The optimal technical strategy is to follow the individual market profit drive, which is very likely, at the same time for the social good, to achieve high PV yields with limited biomass losses and more significant crop water-saving effects. The local food, water, and energy demands are also included as a metric for resource allocation on the potential side. Besides focusing on the biomass-PV tradeoff simulation and analysis, pioneer works have also been done to test the transferability of the method in cases outside Germany, and the complement of urban solid waste to agricultural biomass is explored to achieve energy autarky

A framework for evaluating the impact of communication on performance in large-scale distributed urban simulations

A primary motivation for employing distributed simulation is to enable the execution of large-scale simulation workloads that cannot be handled by the resources of a single stand-alone computing node. To make execution possible, the workload is distributed among multiple computing nodes connected to one another via a communication network. The execution of a distributed simulation involves alternating phases of computation and communication to coordinate the co-operating nodes and ensure correctness of the resulting simulation outputs. Reliably estimating the execution performance of a distributed simulation can be difficult due to non-deterministic execution paths involved in alternating computation and communication operations. However, performance estimates are useful as a guide for the simulation time that can be expected when using a given set of computing resources. Performance estimates can support decisions to commit time and resources to running distributed simulations, especially where significant amounts of funds or computing resources are necessary. Various performance estimation approaches are employed in the distributed computing literature, including the influential Bulk Synchronous Parallel (BSP) and LogP models. Different approaches make various assumptions that render them more suitable for some applications than for others. Actual performance depends on characteristics inherent to each distributed simulation application. An important aspect of these individual characteristics is the dynamic relationship between the communication and computation phases of the distributed simulation application. This work develops a framework for estimating the performance of distributed simulation applications, focusing mainly on aspects relevant to the dynamic relationship between communication and computation during distributed simulation execution. The framework proposes a meta-simulation approach based on the Multi-Agent Simulation (MAS) paradigm. Using the approach proposed by the framework, meta-simulations can be developed to investigate the performance of specific distributed simulation applications. The proposed approach enables the ability to compare various what-if scenarios. This ability is useful for comparing the effects of various parameters and strategies such as the number of computing nodes, the communication strategy, and the workload-distribution strategy. The proposed meta-simulation approach can also aid a search for optimal parameters and strategies for specific distributed simulation applications. The framework is demonstrated by implementing a meta-simulation which is based on case studies from the Urban Simulation domain

A framework for evaluating the impact of communication on performance in large-scale distributed urban simulations

A primary motivation for employing distributed simulation is to enable the execution of large-scale simulation workloads that cannot be handled by the resources of a single stand-alone computing node. To make execution possible, the workload is distributed among multiple computing nodes connected to one another via a communication network. The execution of a distributed simulation involves alternating phases of computation and communication to coordinate the co-operating nodes and ensure correctness of the resulting simulation outputs. Reliably estimating the execution performance of a distributed simulation can be difficult due to non-deterministic execution paths involved in alternating computation and communication operations. However, performance estimates are useful as a guide for the simulation time that can be expected when using a given set of computing resources. Performance estimates can support decisions to commit time and resources to running distributed simulations, especially where significant amounts of funds or computing resources are necessary. Various performance estimation approaches are employed in the distributed computing literature, including the influential Bulk Synchronous Parallel (BSP) and LogP models. Different approaches make various assumptions that render them more suitable for some applications than for others. Actual performance depends on characteristics inherent to each distributed simulation application. An important aspect of these individual characteristics is the dynamic relationship between the communication and computation phases of the distributed simulation application. This work develops a framework for estimating the performance of distributed simulation applications, focusing mainly on aspects relevant to the dynamic relationship between communication and computation during distributed simulation execution. The framework proposes a meta-simulation approach based on the Multi-Agent Simulation (MAS) paradigm. Using the approach proposed by the framework, meta-simulations can be developed to investigate the performance of specific distributed simulation applications. The proposed approach enables the ability to compare various what-if scenarios. This ability is useful for comparing the effects of various parameters and strategies such as the number of computing nodes, the communication strategy, and the workload-distribution strategy. The proposed meta-simulation approach can also aid a search for optimal parameters and strategies for specific distributed simulation applications. The framework is demonstrated by implementing a meta-simulation which is based on case studies from the Urban Simulation domain

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity