Article

Automated urban energy system modeling and thermal building simulation based on OpenStreetMap data sets

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Abstract

City districts have a large potential to reduce greenhouse gas emissions by usage of energy efficiency measures. Urban energy models (UEM) can be useful to analyze the impact of different energy efficiency actions on city districts. While simulation of demand data with high spatial and temporal resolution is often necessary to evaluate retrofit measures, the city's complex structure and lack of data often prevents a reliable application of such methods. This paper presents an urban energy modeling approach based on open-source geographical information system (GIS) datasets to reduce input data uncertainty and simplify city district modeling. We present a method to automatically extract basic city district data from OpenStreetMap (OSM) and enrich these datasets based on national building stock statistics. Building models with representative geometries and physical properties are automatically generated based on building archetype information. These models enable thermal simulation on urban scale. The approach is demonstrated for a use case in Germany, where a reference city district model has been generated with OSM data extraction and enrichment. The reference city district model has been used to perform a space heating net energy demand uncertainty analysis. The demand values simulated with the reference model show a sufficient fit with measured consumption values. The approach provides a fast and structured methodology to model city districts and simulate space heating energy demand on urban scale.

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... Building's energy consumption account for about 30% of the world's energy consumption and 60% of this is due to heating and cooling demand [2]. Predicting the energy use of urban buildings and integrating with the control strategies can be beneficial to reduce greenhouse gas (GHG) emissions [3]. UBEM is utilized to analyze and simulate the built environment energy consumption [4]. ...
... However, CityBES oversimplifies the building's geometry, and the archetype selection and assignment are not very accurate. Schiefelbein et al. developed an automated UBEM approach that is GIS-based data extracted from OpenStreetMap to model the city district [3]. They used building archetypes information to enrich the city buildings geometry dataset to simulate buildings thermal loads in the urban context. ...
... 2 Introducing a new detailed workflow for developing a detailed building construction library for energy demand calculation. 3 Integrating the energy-related data from different sources such as municipalities, utilities, etc., using the spatial join technique. 4 Introducing a new detailed archetype selection and archetype assignment to the buildings. ...
Article
Cities play an essential role in energy consumption and its environmental impacts. Urban Building Energy Modeling (UBEM) can help optimize the built environment's energy efficiency and improve the design and operation of building energy systems. In order to develop UBEM, individual buildings' characteristics such as constructions, internal loads, energy systems, etc., are required. To develop a comprehensive urban building energy model requires detailed 3D urban building geometry information, comprehensive building attribute libraries and a detailed archetype selection to automatically assign parameters to the building construction and usage. Most of the already developed tools do not take full advantage of external data sources of building characteristics with different formats and integrate them into the UBEM workflow. This study introduces a workflow to automatically extract, collect, and pre-process the energy-related parameters from open-source data to enrich the UBEM using spatial joining of attributes and detailed archetype selection. Two different 3D urban models (low and high resolution) are proposed to be used for urban building energy simulation. The workflow using the high-resolution model is demonstrated by applying it to the downtown Montreal buildings as a Canadian case study. A novel method for assigning the building's attributes to the building surfaces and thermal zones is developed, which is based on a detailed and automatic archetype selection. The archetype-selected data and other required information for urban building energy demand calculation are fed into EnergyPlus by introducing a hierarchical concept. The high-resolution enriched UBEM is calibrated using monthly measured data of a reference building, which resulted in an acceptable root mean square error. The method was then applied to the whole district, and it could be shown that ventilation and infiltration rates have the highest impact on energy demand. This study shows that using high-resolution UBEM allows detailed urban building energy analysis, which helps decision-makers to better understand their built environment.
... The first option for enhancing the existing bottom-up archetype libraries is fine-scale archetype library development based on archetype segmentation and description (Hong et al., 2016). The second possibility is to improve the archetype quality, such as by using probabilistic characterization methods (Schiefelbein et al., 2019) or Bayesian calibration (Sokol et al., 2017;Wang et al., 2020); related research results suggest that optimized archetypes are more accurate than standard ones. Furthermore, with the advancement of interdisciplinary collaborations in artificial intelligence and data mining technologies such as machine learning (Fathi et al., 2020), the quality of data used in building archetype development and the optimization of archetype descriptions have improved in recent years. ...
... Specifically, protocols must be developed to increase transparency regarding model elements, development, and validation (Hong et al., 2018). Much effort is needed to optimize the UBEM EnergyPlus (Abolhassani et al., 2022;Alajmi & Phelan, 2020;Bass et al., 2022;Braulio-Gonzalo et al., 2016;Buckley et al., 2021;Cerezo et al., 2015;Deng et al., 2022;Hong et al., 2016;Johari et al., 2023;Monteiro et al., 2015;Srinivasan et al., 2020;Zygmunt & Gawin, 2021) Other physics-based model Katal et al., 2019;Mutani & Todeschi, 2020;Österbring et al., 2016;Prataviera et al., 2021;Siller et al., 2007;Todeschi et al., 2021;Todeschi et al., 2022) Reduced-order RC model (Buffat et al., 2017;İşeri & Dino, 2020;Li et al., 2020;Wang et al., 2020;Todeschi et al., 2021;) Regressive model (Ali et al., 2019;Bianchi et al., 2020;Canyurt et al., 2005;Dall'O' et al., 2012;Ghiassi et al., 2015;Howard et al., 2012;Mastrucci et al., 2014;Polly et al., 2016;Shkurti, 2018) Others (Adam & Badea, 2017;Ascione et al., 2013;Bentzen & Engsted, 2001;Haneef et al., 2021;Hedegaard et al., 2019;Katal et al., 2022;Mata et al., 2014;Nagpal & Reinhart, 2018;New et al., 2017;Nutkiewicz et al., 2018;Panão & Brito, 2018;Remmen et al., 2018;Rubeis et al., 2021;Schiefelbein et al., 2019;Sokol et al., 2017;Summerfield et al., 2010;Wang et al., 2021) Annual 59% ...
Article
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In recent decades, urban energy consumption and carbon emissions have expanded rapidly on a global scale. Building sector, in particular, accounts for approximately 40% of overall energy use. Urban planners and decision-makers have a significant responsibility to achieve sustainable energy and climate objectives. Urban building energy modeling (UBEM) has increased in popularity in recent years as a tool for calculating urban-scale energy use in buildings with limited resources, and that facilitated the formulation of new energy policies. However, published studies of UBEM methodologies and tools lack comprehensive examinations of the potential limitations of research and the prospects of future opportunities. This paper provides a complete conceptual framework for UBEM based on extensive literature reviews and prior researchers’ work. In addition to providing a comprehensive understanding of the various UBEM approaches and tools, future research directions are explored. The results demonstrate that earlier researches did not adequately account for input uncertainty and lacked proper simulation and calibration control for algorithms/models. These challenges not only increased the workload and computational burden of modelers but also diminished the precision of model calculations. In response, this paper provides targeted recommendations for each essential phase of the present UBEM workflow, namely model input, model development, and model calibration, to address these limitations, as well as a comprehensive analysis of future prospects. The main aim of the research is to further UBEM development as a faster, more accurate and multiscale supportive tool and establish a framework for future UBEM methods.
... The accuracy of the data and tthe process of data processing have an impact on the effective use of UBEM. On the other hand, the two main challenges in the UBEM process are the lack of existing data and the difficulties in detecting stochastic data [14,15]. A UBEM created in high resolution allows for detailed urban building energy analyzes where decision makers can better read the space [16]. ...
... Bayes ensures the accuracy of the analysis where there is measured data for comparison with the analysis result. Uncertainty analysis can provide a distribution of possible demand values at the building scale, which can be useful when users do not have reference consumption values [14]. While uncalibrated physics-based modeling methods are very likely to contain errors, models using Bayesian calibration have consistently detected lower errors in hourly temporal resolution [93]. ...
Article
Fossil fuels increase the emission values of greenhouse gases such as CO2 in the atmosphere and cause global warming and climate change. At the same time, fossil fuel reserves are facing depletion in the near future, and energy supply also has an important dimension such as national security and foreign dependency. All these show that turning to renewable energy sources and developing solutions and policies for energy saving has become a necessity both globally and locally. For such reasons, modeling of urban structures, which have a great contribution to energy consumption, and simulating the energy demand on an urban scale are of great importance for the effective use of energy. Research on this has shown that UBEM (Urban Building Energy Modeling) is an effective solution to these problems. However, UBEM contains different technical problems for implementation. Due to its versatility, various concepts related to this field lead to complexity. With this increasing complexity, there is a growing need to compile concepts from a holistic perspective. In this study, it is aimed to create a solution to these challenges. For this purpose, a comprehensive and up-to-date research of various modeling approaches and model creation process used in urban building energy modeling has been conducted. Studies on these approaches are summarized and a systematic review of the literature is made. At the same time, the study is in the nature of guiding and forming the general knowledge level with the basic concepts that should be known to those who will work on UBEM.
... The author proposed a two-step methodology in which the results from the physics-based approach were adjusted using a regression analysis. Last but not least, it should be noted that most of these bottom-up models have integrated Geographical Information System (GIS) tools in their analysis (Calderón et al., 2015; Y. Dall'o' et al., 2012;Fichera et al., 2016;García-Pérez et al., 2018;Gargiulo et al., 2017;Mastrucci et al., 2014;Reiter and Marique, 2012;Schiefelbein et al., 2019;Torabi Moghadam et al., 2018), making the data collection and treatment easier for modellers, and providing energy planners key data to inform and support the decision-making process (Alpagut et al., 2021;Li, 2017; Urrutia-Azcona et al., 2021). ...
... Following a bottom-up physics-based approach, several authors have clustered the building stock of a city through the definition of archetypes which are then scale-up to represent the energy use of the whole sector at city level(Fernandez et al., 2020;Kim et al., 2019;Schiefelbein et al., 2019;Torabi Moghadam et al., 2018;Uidhir et al., 2020). A building archetype represents a group of buildings with similar properties(Abbasabadi and Mehdi Ashayeri, 2019;Reinhart and Cerezo Davila, 2016). ...
Thesis
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This Thesis aims to develop a comprehensive framework for integrated long-term urban energy planning based on the modelling and prospective assessment of urban energy systems. This work brings the opportunity to solve the various challenges faced by urban energy modellers, as well as to supply policy-makers and urban planners with tools for the achievement of energy and climate objectives. Indeed, this Thesis seeks to shed light regarding specific issues and gaps faced when assessing urban energy systems, see the lack of clear approaches to develop comprehensive urban energy models and to incorporate their results into urban energy plans, the complexity and uncertainty when modelling future urban energy use, energy data scarcity at urban level, and the need of harmonised local and national energy and climate plans.
... Urban Building Energy Models (UBEMs) have been developed and utilised for many years Elmar Reiter (1980); Rickaby (1991); Adolphe (2001), but their plethora has significantly been increased over the last decade Swan and Ugursal (2009); Reinhart and Cerezo Davila (2016); Malhotra et al. (2022). Making use of existing open source software like Energy-Plus Sokol et al. (2017) or creating customised modelling suites Fonseca and Schlueter (2015); Schiefelbein et al. (2019), researchers have been able to analyse the energy demand of urban areas spanning from a small number of buildings Nageler et al. (2018) to millions Krarti et al. (2020), transforming UBEMs to a powerful tool which can be used in numerous applications Ang et al. (2020). One of the benefits in recent years has been the increased availability and accessibility to large datasets, which can be configured accordingly to fit the needs of individual models, using open source platforms and software Malhotra et al. (2022); Schiefelbein et al. (2019). ...
... Making use of existing open source software like Energy-Plus Sokol et al. (2017) or creating customised modelling suites Fonseca and Schlueter (2015); Schiefelbein et al. (2019), researchers have been able to analyse the energy demand of urban areas spanning from a small number of buildings Nageler et al. (2018) to millions Krarti et al. (2020), transforming UBEMs to a powerful tool which can be used in numerous applications Ang et al. (2020). One of the benefits in recent years has been the increased availability and accessibility to large datasets, which can be configured accordingly to fit the needs of individual models, using open source platforms and software Malhotra et al. (2022); Schiefelbein et al. (2019). However, even in data-rich urban settings, the level of detail in large scale datasets is often limited. ...
Conference Paper
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By 2050 urban population is estimated to grow from 4 billion to almost 7 billion, with over 90% expected in the Global South, where development often takes place as unplanned informal settlements, with essential shortage of critical infrastructure. In processing some of the associated rising challenges, Urban Building Energy Models can play a key role. However, such models have had limited presence in this context, highlighting the inequalities in the representation of such communities in this field. This paper works towards addressing this gap and presents the development of an Urban Building Energy Modelling workflow for analysing the thermal comfort in a self-constructed, low-income housing neighbourhood in Lima, Peru, using an innovative approach, based largely on open source software, such as EnergyPlus, QGIS and Python. The results highlight that the compact and dense built form of the building blocks, can cause higher heat retention, especially in lower thermal zones and therefore result in high indoor temperatures for longer. Additionally, the poor thermal performance of the buildings’ fabric, can cause hourly indoor temperatures to rise to critical levels, especially in higher thermal zones, which can have adverse impacts on the residents’ health. This first step in understanding some of the key issues these communities are facing, is critical in the early assessment of future building retrofit decisions.
... This oversimplified archetype characterization led to a 10% prediction error [20]. Using a simplified methodology in dynamic building simulation through the TEASER platform [34], Schiefelbein et al. created a Python-based framework that accelerates the UBEM development [35]. A simple deterministic characterization was applied to the model. ...
... However, the sophisticated occupancy profiles adapted from Richardson et al. [36] and some physical parameters were stochastically characterized. While the averaged simulation results were satisfactory, the validation process over the metered data remains weak and complicated [35]. ...
Chapter
Bottom-up modeling appears to be a suitable approach for the urban-scale building energy performance assessment with providing valuable inferences on the complicated building energy patterns and helping authorities monitor/predict the energy demand for urban planning and retrofitting. Archetype characterization is the utmost challenging process when developing bottom-up models since there is a large diversity in characteristic features of building stocks. This gap induces practitioners to seek stochastic methods even though the deterministic approaches are solid guides in archetype characterization. Hence, the research objective of this study is to provide insights into the motivation, challenges, and methods of the studies conducted to assess the buildings' energy demand at the urban scale. The original value of this research is to analyze/question different archetype characterization methods and their practicability over wide-ranging studies, identify the most crucial characterization parameters and assess the validation techniques to enhance the demand estimations of urban building energy models (UBEMs). To that end, this study performs a literature review and mainly provides the following findings: (1) The required characterization method is highly dependent on the purpose and scope of the study. (2) The Bayesian calibration makes ground in UBEM practices as it consolidates the models' estimation power through the probabilistic archetype characterization. (3) Considering the notable fluctuations in buildings' energy demand induced by occupancy patterns, detailed occupancy profiles could improve the archetype characterization. Finally, the major setback is the lack of available data to characterize energy models with building-specific information. (4) Building information models (BIMs) could soon play a pivotal role in supplying such data for UBEM practices. This study contributes to the literature by fulfilling the lack of perspective that concentrates on the archetype characterization methods in UBEM. The findings could help practitioners (e.g., policymakers and city planners) and academics to comprehend the potential of the UBEM that improves energy management strategies at the urban scale.KeywordsUrban building energy modeling (UBEM)Archetype characterizationOccupancy-related uncertainties
... However, as the OSM is still in development, the availability of building footprints is insufficient for UBEM at present. Therefore, OSM is usually treated as a supplement to other databases in UBEM studies [26,40,56,79]. ...
... Wang et al. [78] found that just 1 out of 1133 buildings in a case study in Nanjing (China) contained building heights. In addition, Schiefelbein et al. [79] claimed that data enrichment has to be done due to the incompleteness of building heights in the OSM. Based on these facts, it seems impossible to acquire the building heights merely from the OSM. ...
Article
Urban Building Energy Modeling (UBEM) is essential for urban energy-related applications. Its generation mainly requires four data inputs, including geometric data, non-geometric data, weather data, and validation and calibration data. A reliable UBEM depends on the quantity and accuracy of the data inputs. However, the lack of available data and the difficulty in determining stochastic data are two of the main barriers in the development of UBEM. To bridge the research gaps, this paper reviews appropriate acquisition approaches for four data inputs, learning from both building science and other disciplines such as geography, transportation and computer science. In addition, detailed evaluations are also conducted in each part of the study, and the performance of the approaches are discussed, as well as the availability and cost of the implemented data. Systematic discussion, multidisciplinary analysis and comprehensive evaluation are the highlights of this review.
... This oversimplified archetype characterization led to a 10% prediction error [20]. Using a simplified methodology in dynamic building simulation through the TEASER platform [34], Schiefelbein et al. created a Python-based framework that fastens the UBEM development [35]. A simple deterministic characterization was applied to the model. ...
... However, the sophisticated occupancy profiles adapted from Richardson et al. [36] and some physical parameters were stochastically characterized. While the averaged simulation results were satisfactory, the validation process over the metered data remains weak and complicated [35]. ...
Conference Paper
Full-text available
Bottom-up modeling appears to be a suitable approach for the urban-scale building energy performance assessment with providing valuable inferences on complicated building energy patterns and helping authorities moni-tor/predict the energy demand for urban planning and retrofitting. Archetype characterization is the utmost challenging process when developing bottom-up models since there is a large diversity in characteristic features of building stocks. This gap induces practitioners to seek stochastic methods even though the deterministic approaches are solid guides in archetype characterization. Hence, the research objective of this study is to provide insights into the motivation , challenges, and methods of the studies conducted to assess the buildings' energy demand at the urban scale. The original value of this research is to ana-lyze/question different archetype characterization methods and their practicability over wide-ranging studies, identify the most crucial characterization parameters and assess the validation techniques to enhance the demand estimations of urban building energy models (UBEMs). To that end, this study performs a literature review and mainly provides the following findings: (1) The required characterization method is highly dependent on the purpose and scope of the study. (2) The Bayesian calibration makes ground in UBEM practices as it consolidates the models' estimation power through the probabilistic archetype characterization. (3) Considering the notable fluctuations in buildings' energy demand induced by occupancy patterns, detailed occupancy profiles could improve the archetype characterization. Finally, the major setback is the lack of available data to characterize energy models with building-specific information. (4) Building information models (BIMs) could soon play a pivotal role in supplying such data for UBEM practices. This study contributes to the literature by fulfilling the lack of perspective that concentrates on the archetype characterization methods in UBEM. The findings could help practitioners (e.g., policymak-ers and city planners) and academics to comprehend the potential of the UBEM that improves energy management strategies at the urban scale.
... It is implemented in the Python 3 programming language and licensed under the MIT license. The framework builds on the pycity_base package [10] and is available from the Python Project Index (PyPI), see [11]. We chose the Python programming language for the framework, because it is open-source, platformindependent, widely used in academia, and allows scientists to use and contribute to it easily. ...
... To satisfy and consolidate these three crucial capabilities within the pycity_scheduling framework, our implementations adapt the distributed power dispatch coordination approach including its mathematical formulation from the seminal work in [9] and map it to the field of multi-energy system applications with the support of base package pycity_base [10]. Fig. 1 illustrates this adaptation. ...
Article
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We introduce the open-source Python software framework pycity_scheduling for the effective development, testing, and assessment of optimisation-based power scheduling algorithms for local multi-energy systems in city districts. The framework primarily targets the elaboration of coordination concepts that can efficiently solve the power dispatch problem on the city district level. Its target users are researchers in the field of smart grid applications and the deployment of operational flexibility for local energy systems. Illustrative code examples demonstrate the capabilities of the pycity_scheduling framework and its use cases. The design principles established in pycity_scheduling allows users to access, extend, and modify the Python package without any need for commercial software or licensing concerns.
... However, leveraging data collection methods from other disciplines offers potential solutions. Mapping platforms, notably OpenStreetMap, can supply building footprint data essential for UBEM (Chen & Hong, 2018;Schiefelbein et al., 2019). Cell phone data helps characterize building occupancy (Barbour et al., 2019;Pang et al., 2018), a key determinant of energy use. ...
Article
Full-text available
Assessing building energy consumption in urban neighborhoods at the early stages of urban planning assists decision-makers in developing detailed urban renewal plans and sustainable development strategies. At the city-level, the use of physical simulation-based urban building energy modeling (UBEM) is too costly, and data-driven approaches often are hampered by a lack of available building energy monitoring data. This paper combines a simulation-based approach with a data-driven approach, using UBEM to provide a dataset for machine learning and deploying the trained model for large-scale urban building energy consumption prediction. Firstly, we collected 18,789 neighborhoods containing 248,938 buildings in the Shanghai central area, of which 2,702 neighborhoods were used for UBEM. Simultaneously, building functions were defined by POI data and land use data. We used 14 impact factors related to land use and building morphology to define each neighborhood. Next, we compared the performance of six ensemble learning methods modeling impact factors with building energy consumption and used SHAP to explain the best model; we also filtered out the features that contributed the most to the model output to reduce the model complexity. Finally, the balanced regressor that had the best prediction accuracy with the minimum number of features was used to predict the remaining urban neighborhoods in the Shanghai central area. The results show that XGBoost achieves the best performance. The balanced regressor, constructed with the 9 most contributing features, predicted the building rooftop photovoltaics potential, total load, cooling load, and heating load with test set accuracies of 0.956, 0.674, 0.608, and 0.762, respectively. Our method offers an 85.5% time advantage over traditional methods, with only a maximum of 22.75% of error.
... The electricity and occupancy profiles serve as input for a time-resolved internal gain calculation. Additionally, the occupancy profiles are needed for domestic hot water profile generation, for which functions from the pyCity tool [Schiefelbein et al., 2019] are utilized. Finally, the static building data, as well as the time-resolved weather and internal gain data, are included in the space heating profile generation. ...
Preprint
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The Districtgenerator enables an automated generation of time-resolved load profiles for residential districts. The profiles generated for each building within the district are the demands for electricity, space heating demand, domestic hot water, and the occupancy profiles. Additionally, a heating load calculation is carried out for each building. The Districtgenerator is conceptualized using a minimum amount of input data. Thus, the tool is valuable for researchers and planners to obtain information needed, for example, for the energy system or energy management system design in an early planning phase of a district.
... By combining OSM data with non-spatial data such as energy usage, building density, and infrastructure development, businesses and governments can able to develop well-informed strategies to meet energy demands efficiently. For example, Schiefelbein et al., (2019) presented an urban energy modeling technique by incorporating OSM data to assess the effects of various energy efficiency measures on urban districts. Similarly, Saha et al., (2019) introduced a framework for generating synthetic distribution feeders that align with real geospatial topologies, utilizing existing OpenStreetMap data. ...
Chapter
OSM plays a crucial role in advancing sustainable development across the economy, environment, and society. It can foster economic sustainability by enabling infrastructure planning, disaster response, and energy efficiency. Moreover, OSM can contribute to environmental sustainability through resource management, climate change mitigation, and biodiversity protection. Additionally, OSM can promote social sustainability by empowering communities and supporting healthcare, education, and gender equality. Spatial analysis of OSM data can help to identify areas that need essential services, addressing social inequalities. OSM's vast capabilities, coupled with cloud computing, can further enhance its role in driving sustainable progress. Continuous efforts are required to enhance data accuracy and completeness in OSM using advanced techniques like machine learning and artificial intelligence while also promoting inclusive mapping involving marginalized communities.
... Several other studies use the open tool TEASER (Remmen et al. 2018), which offers data enrichment and export of Modelica simulation models based on predefined input parameters or CityGML models: Fuchs et al. (2016) use GIS data combined with a PostgreSQL database to automatically parametrise building models and simulate the heat demand in Modelica language with TEASER. Schiefelbein et al. (2019) present a method to automatically extract basic data from Open-StreetMap (GIS data), enrich it based on statistics, and generate Modelica building models based on archetype information from TEASER. Gorzalka et al. (2021) developed an approach for automatic generation of dynamic energy simulation models in Modelica for a single existing building using an aerial image and TEASER. ...
Article
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Energy simulation models are crucial to estimate the energy demand of buildings, especially for prospective planning on a district or city scale. As required input data is not available in many cases, an automated model generation workflow is needed. Existing workflows have several disadvantages, including: (i) dependence on large input datasets of existing buildings; (ii) no 3D representation to support the planning process; (iii) they are proprietary solutions. The pipeline ‘SHP2SIM’ is an open-source python pipeline enabling enrichment and generation of building energy simulation models based on little input data for district and urban scale. The pipeline is tested by simulating the heat load for a district with 27 buildings and validated for one building: R squared is 0.9825, CV(RMSE) is 22.10%, and NMBE is 4.06% on a monthly basis. To enable reproducibility and encourage open science, input data, output models, and the pipeline are openly available (https://github.com/tug-cps/shp2sim).
... The specific thermal demands of different building types is chosen according to the TABULA Project [31] for residential buildings. Using these specific demands, the annual thermal demands listed in Table 3, and heating profiles are obtained using the PyCity [34] tool. To generate non-residential heat profiles, standard profiles are scaled by considering the annual energy consumption parameters for the corresponding building types [35]. ...
Article
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Local Energy Communities (LECs) provide a framework for community-oriented use of prosumer-owned renewable generation and storage units. For LECs to gain acceptance and eventually become widespread actors in future energy systems, their impacts on the grid and its participants must be investigated. However, the scientific landscape lacks sufficient quantitative analyses that specifically investigate the effects of LECs on the underlying grid infrastructure. This work conducts a case study to understand the system effects that may arise from the local cooperation of prosumers in future low voltage systems in Germany. To analyze these effects, performance metrics are defined, which help to compare the energy neutrality and grid reliability of low voltage (LV) grids with and without local energy exchanges. Furthermore, representative test grids are synthesized by considering the existing (2020) and predicted future (2030) distribution of energy assets, such as photovoltaics, battery storage, electrical heat pumps, and electric vehicles in Germany. For these grids, simulations considering different scenarios, including both the reference and community behavior of their participants, are conducted, and the defined performance metrics are applied. The simulations show that the LEC provides significant benefits in terms of energy neutrality and grid reliability. In this way, this study complements ongoing efforts in the design of LECs and incentives for customers, for example, in the form of favorable tariffs for locally generated energy.
... The occupancy and corresponding electricity profiles are generated by a stochastic model based on [30]. The occupancy data is used to model the heating demand by modeling the domestic hot water consumption based on [31]. The space heating demand calculations are based on a 5R1C model according to EN ISO 13790 [32], where the corresponding building parameters are given by TABULA archetype buildings [33]. ...
... Using the TEASER tool, envelope areas and building physics parameters are calculated based on building type, floor area and construction year [18]. Additionally, it simulates thermal demands using a 5R1C model, while generating annual electrical load profiles and domestic hot water profiles through stochastic methods [19][20][21]. The building energy systems within the neighborhood contain electricity based heat generation devices for the heat provision. ...
... Open-StreetMap datasets were used in [25] to model and simulate energy systems of city district buildings. Using this GIS-based approach and the Python tool TEASER, an automated urban energy system model was generated. ...
Article
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The digital transition that drives the new industrial revolution is largely driven by the application of intelligence and data. This boost leads to an increase in energy consumption, much of it associated with computing in data centers. This fact clashes with the growing need to save and improve energy efficiency and requires a more optimized use of resources. The deployment of new services in edge and cloud computing, virtualization, and software-defined networks requires a better understanding of consumption patterns aimed at more efficient and sustainable models and a reduction in carbon footprints. These patterns are suitable to be exploited by machine, deep, and reinforced learning techniques in pursuit of energy consumption optimization, which can ideally improve the energy efficiency of data centers and big computing servers providing these kinds of services. For the application of these techniques, it is essential to investigate data collection processes to create initial information points. Datasets also need to be created to analyze how to diagnose systems and sort out new ways of optimization. This work describes a data collection methodology used to create datasets that collect consumption data from a real-world work environment dedicated to data centers, server farms, or similar architectures. Specifically, it covers the entire process of energy stimuli generation, data extraction, and data preprocessing. The evaluation and reproduction of this method is offered to the scientific community through an online repository created for this work, which hosts all the code available for its download.
... Using the TEASER tool, envelope areas and building physics parameters are calculated based on building type, floor area and construction year [18]. Additionally, it simulates thermal demands using a 5R1C model, while generating annual electrical load profiles and domestic hot water profiles through stochastic methods [19][20][21]. ...
Preprint
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Local energy markets (LEMs) are a promising way to solve the challenges of the increasing extension of decentralized energy systems and to promote the further integration of renewable energy sources. LEMs enable costumers with distributed energy resources to trade and share their electrical energy with each other. In the existing literature, the research focus is mostly on the development and evaluation of specific elements of LEMs, such as bidding strategies or market designs. The paper contributes a comprehensive evaluation of a LEM and the quantification of its benefit regarding the market-based device operation. For the evaluation in terms of financial outcome and local energy exchange, a centralized and a decentralized operation optimization serve as upper and lower references. In centralized optimization, the system boundary comprises the entire neighbourhood. In decentralized optimization, each building is balanced separately. For the LEM, we introduce a distributed market design with the involvement of an auctioneer. We focus there on the implementation of learning bidding strategies and a double-sided auction with non-iterative market clearing rules. For all three energy management techniques, the operating schedules of the devices are determined using mixed-integer linear programming. In several case studies we investigate different neighbourhoods in order to evaluate the influence of different technologies and their penetrations as well as the impact of the building stock in terms of building type and construction year. We evaluate the market outcome with multiple key performance indicators (KPIs) such as the supply-and demand-cover-factor, the total operation costs and the peak load. The results show that total energy costs can be reduced by up to 6.4 %. For the energy exchange, it is shown that the electricity surplus is up to 72 % and the electricity demand of the neighbourhood decreases by up to 6.8 % compared to the decentralized optimization and increases by up to 14.3 % compared to the centralized optimization. Further, we noted up to 46.2 % higher peak loads.
... These 3D models are also starting to be used to perform simulations at urban level. For example, in Schiefelbein et al. (2019) a method is presented to automatically extract basic city district data from OpenStreetMap (OSM), using national building stock statistics to complete the GIS datasets. In Ascione et al. (2021), a GIS tool is coupled with SketchUp to generate geometrical models of the buildings, then DesignBuilder is used for the thermophysical definition of the building envelopes, then EnergyPlus is used to perform the simulations, and lastly Matlab is used to process the results. ...
Article
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Current approaches for simulating the energy performance of buildings on a large scale are limited by numerous assumptions and simplifications, which can lead to inaccurate estimations. While new tools and procedures are emerging to improve accuracy, there remains a need for more user-friendly methods. This study proposes a new tool based on online maps to create the geometry of districts in a simple way. The tool also enables an automatic evaluation of all buildings through dynamic hourly simulations, using a building simulation software and allowing to consider different weather conditions. To illustrate the procedure, a district at risk of energy poverty in Seville (Spain) is modeled, where hourly temperature data for a whole year are available to demonstrate the need for building improvements. The tool is used to evaluate the energy demands of the district under several retrofitting alternatives, and free-floating simulations are also performed to evaluate the improvement of thermal comfort without air-conditioning systems. The aim is not to discuss the actual values for this particular case, but rather to identify the correct direction for large-scale studies, so as to make them more easily conducted. Overall, it may be concluded that the results provided by comprehensive tools, such as the one proposed in this study, enable easy yet accurate evaluations of buildings on a large scale with significant time savings, as well as the identification of locations where retrofitting interventions would have the greatest impact.
... However, when considering large urban areas or even an entire city, it will be necessary either to have access to the specific building end-uses, or to utilize statistical and census data to categorize the number of buildings using the different typologies in terms of construction period and residential or non-residential use. For example, a similar study [54], used OpenStreetMap to extract relevant city district data, which were then complemented with statistical data from the national building stock and coupled with the energy performance of archetypal buildings. ...
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Data collection and large-scale urban audits are challenging and can be time consuming processes. Geographic information systems can extract and combine relevant data that can be used as input to calculation tools that provide results and quantify indicators with sufficient spatial analysis to facilitate the local decision-making process for building renovations and sustainability assessment. This work presents an open-access tool that offers an automated process that can be used to audit an urban area in order to extract relevant information about the characteristics of the built environment, analyze the building characteristics to evaluate energy performance, assess the potential for the installation of photovoltaics on available building rooftops, and quantify ground permeability. A case study is also presented to demonstrate data collection and processing for an urban city block, and the relevant results are elaborated upon. The method is easily replicable and is based on open data and non-commercial tools.
... Building data from this source, which can be mapped at different scales and detail and may contain a rich set of attributes describing the individual building stock, has been welcomed by the built environment research community thanks to the increasing coverage, quality, open licence, and uniqueness, as OSM remains the only such building data source worldwide. For example, building data available in OSM has been used for numerous studies in the built environment, e.g. on vulnerability and damage assessment [21][22][23][24], energy modelling and thermal simulations [25][26][27][28][29][30], microclimate studies [31][32][33][34], water and waste management [35, https://doi. ...
Article
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Geospatial data of the building stock is essential in many domains pertaining to the built environment. These datasets are often provided by governments, but crowdsourcing them has surged in the last decade. Nowadays, OpenStreetMap (OSM) — the most popular Volunteered Geographic Information (VGI) platform — contains geospatial and descriptive data on more than 500 million buildings worldwide collected by millions of contributors, and it is increasingly used in studies ranging from energy and microclimate to urban planning and life cycle assessment. However, large-scale understanding on their quality remains limited, which may hinder their use and management. In this paper, we seek to understand the state of building information in OSM and whether it is a reliable source of such data. We provide a comprehensive study to assess the quality of attribute (descriptive) data of the building stock mapped globally, e.g. building function, which are key ingredients in many analyses and simulations in the built environment. We examine three aspects: completeness, consistency, and accuracy. In this assessment, the first at such scale and the most comprehensive available hitherto, we find that quality continues to be highly heterogeneous — from poor quality in some, to very high completeness in other areas, potentially benefiting a range of application domains, e.g. we estimate that 3D building models of 443 administrative units (mostly cities and municipalities) around the world can be generated from OSM, underpinning the generation of digital twins. The number of floors and building type are the most frequent properties that contributors record, and in most cases are highly accurate, while mapping the interior of buildings did not gain momentum.
... 85 By leveraging Applications Programming Interfaces (API), the building footprint can be automatically extracted. 86 In addition, OSM data could also provide information about building heights. The information about building height can be also acquired through Light Detection and Ranging (LiDAR) surveys and photogrammetric techniques. ...
Article
Current energy and climate policies are formulated and implemented to mitigate and adapt to climate change. To inform relevant building policies, two bottom-up building stock modelling approach: 1) archetype-based and 2) Building-by-building have been developed. This paper presents the main characteristics and applications of these two approaches and evaluates and compares their ability to support policy making. Because of lower data requirements and computational cost, archetype-based modelling approaches are still the mainstream approach to stock-level energy modelling, life cycle assessment, and indoor environmental quality assessment. Building-by-building approaches can better capture the heterogeneous characteristics of each building and are emerging due to the development of data acquisition and computational techniques. The model uncertainties exist in both models which may affect the reliability of outputs, while stochastic archetype models and timeless digital twin model have the potential to address the issue. System dynamics modelling approach can describe and address the dynamics and complexity of often-conflicting policies and achieve co-benefit of multiple policy objectives. This paper aims to provide comprehensive knowledge on building stock modelling for modellers and policymakers, so they could use a building stock model with an appropriate user interface without having to fully understand the underlying algorithms or complexities.
... 8 Already in 2012, OSM was shown to be able to find shorter paths for pedestrians due to the higher completeness of the data compared to commercial providers such as TomTom (Zielstra & Hochmair, 2012). It has also been used repeatedly for scientific research to create, for example, a forest landscape integrity index (Grantham et al., 2020) or to simulate space heating energy demand within cities (Schiefelbein et al., 2019). ...
Article
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The permeability of nation-state borders determines the flow of people and commodities between countries and therefore greatly influences many aspects of human development from trade and economic inequality to migration and the ethnic composition of societies worldwide. While past research on the topic has focused on border fortification (walls, fences, etc.) or the legal dimension of border controls, we take a different approach by arguing that transport infrastructure (paths, roads, railroads, ferries) together with political checkpoints can be used as valuable indicators for the permeability of borders worldwide. More and better transport infrastructure increases permeability, whereas checkpoints create the political capacity for reducing entries. Using automatized computational methods combined with extensive manual checks, we parse data from OpenStreetMap and the World Food Programme to detect cross-border transport infrastructure and checkpoints. Based on this information, we define an index of border permeability for 312 land borders globally. Subsequent analyses show that regardless of the degree of closure enforcement at checkpoints, Europe and Africa have the most, and the Americas the least, permeable borders worldwide. Regression models reveal that border permeability is higher in densely populated areas and that economic development, by far the most relevant explanatory factor, has a curvilinear relationship with border permeability: Borders of very rich and very poor countries are highly permeable, whereas those of moderately prosperous nation-states are significantly harder to cross. Implications of this remarkably clear pattern are discussed.
... OSM data has been used in various studies spanning different fields, including routing [26,27], location-based services [28][29][30], traffic and transportation [31][32][33][34], energy modeling [35,36], population estimation [37,38], 3D city modeling [39][40][41], land cover use [42,43], and emergency response management [44,45]. Ongoing initiatives to improve sidewalk data, including Accessmap [18] and Open-Sidewalks [17], also utilize data from OSM. ...
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Sidewalks play a pivotal role in urban mobility of everyday life. Ideally, sidewalks provide a safe walkway for pedestrians, link public transportation facilities, and equip people with routing and navigation services. However, there is a scarcity of open sidewalk data, which not only impacts the accessibility and walkability of cities but also limits policymakers in generating insightful measures to improve the current state of pedestrian facilities. As one of the most famous crowdsourced data repositories, OpenStreetMap (OSM) could aid the lack of open sidewalk data to a large extent. However, completeness and quality of OSM data have long been a major issue. In this paper, we offer a preliminary study on the availability and trustworthiness of OSM sidewalk data. First, we compare OSM sidewalk data coverage in over 50 major cities in the United States. Then, we select three major cities (Seattle, Chicago, and New York City) to further analyze the completeness of sidewalk data and its features, and to compute a trustworthiness index leveraging historical OSM sidewalk data.
... For each building, we generate the annual electrical load profiles using the richardsonpy, a tool based on the findings of Richardson et al. (2010). The domestic hot water demand is determined via the pyCity tool (Schiefelbein et al., 2019). The heat demand of the individual buildings is calculated by means of the tool TEASER . ...
... Building (footprint) data are essential sources for 3-D modeling (Over et al. 2010, Bagheri et al. 2019, route navigation (Rousell and Zipf 2017), energy use mapping (Alhamwi et al. 2017, Schiefelbein et al. 2019, disaster relief and management , Ghaffarian et al. 2019, and also humanitarian mapping (Herfort et al. 2021). Remote sensing, as a technique to sense objects from satellite imagery, has been widely used for acquiring building data (Yuan et al. 2018). ...
Article
OpenStreetMap (OSM) is currently an important source for building data, despite the existence of potential quality issues.Previous studies have assessed OSM data quality by comparing it with reference building data, which may not otherwise be readily available. This study assessed OSM building completeness using population data, and investigated the effectiveness of using population data for building reference data. We proposed various approaches, including type-based and regression-based approaches and their subtypes, and designed measures and methods to evaluate these approaches. Our evaluation examined four study areas in two countries, using global population data sets at three spatial resolutions (1-km, 100-m, and 30-m). Results showed that the type-based approach correctly classified approximately 80–99% of the assessed grid cells. The regression-based approach resulted in a high linear correlation (0.7 or greater)between the population counts and the referenced building count/building area size, with the strongest correlation present for the 1-km population dataset. We conclude that the use of population data as referenced building data is an effective method for the assessment of OSM building completeness. The paper concludes with the advantages and limitations of using both the type-based and the regression-based approaches.
... Still, as the applications of the UBEM field expand to include more operational aspects, it is inevitable that information regarding the controls of HVAC systems will be regarded as vital. In the reviewed studies, as it can clearly be observed in Fig. 3, this field of input data was majorly neglected, with the main information that could be linked to the control of an HVAC system, being the assumption related to the threshold temperatures of the indoor environment for triggering control actions, often set at 27 • C for cooling and 20 • C for residential heating [59,82]. ...
Article
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The growing demand for energy in urban areas has led to the development of a variety of methodologies for modelling energy in buildings at large scale. However, their accuracy has yet to be thoroughly reviewed. This paper presents a systematic analysis of urban building energy models, that have been validated against measured data, using a singular taxonomy based on key attributes that could influence a model’s accuracy: application, scale, input data, computational method, calibration and validation methods. The analysis showed that the accuracy of urban building energy models is multi-dimensional, considered at a variety of temporal resolutions, spatial resolutions and measures of error, with the results demonstrating that there is no single key attribute that governs it. At the aggregate spatial and annual temporal resolutions, the accuracy, often reported in a single percent error value, can be as low as 1%, while for individual buildings at the annual resolution, the tails of the distribution of errors can reach 1000%. Models using non-calibrated physics-based computational methods were more likely to report overly large errors, while those employing Bayesian calibration consistently reported lower errors at the hourly temporal resolution, demonstrating the positive impact of calibration and in particular the Bayesian approach, on the models’ accuracy. Overall, the review has highlighted that more transparent and consistent reporting of accuracy is necessary and further research is essential for improving the evaluation of accuracy in modelling methodologies, if modern challenges are to be met through emerging applications such as energy systems integration and climate resilience.
... The applicability of using opensource geographical information system (GIS) data for urban energy modeling is assessed by Wang et al. (2021), where buildings' footprints are captured from OpenStreetMap (OSM), and building height for residential buildings are defined based on the story number. Schiefelbein et al. (2019) show the credibility of urban and building energy modeling using geometry from OSM, enriched by building stock statistics as the building performance. Zhou et al. (2021) has performed an impact assessment of UHI and future climate on health risks using urban morphology parameters, including sky view factors, permeable surface fraction, building surface fraction, and building height. ...
Article
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Climate change and urbanization are two major challenges when planning for sustainable energy transition in cities. The common approach for energy demand estimation is using only typical meso-scale weather data in building energy models (BEMs), which underestimates the impacts of extreme climate and microclimate variations. To quantify the impacts of such underestimation on assessing the future energy performance of buildings, this study simulates a high spatiotemporal resolution BEM for two representative residential buildings located in a 600 × 600 m2 urban area in Southeast Sweden while accounting for both climate change and microclimate. Future climate data are synthesized using 13 future climate scenarios over 2010-2099, divided into three 30-year periods, and microclimate data are generated considering the urban morphology of the area. It is revealed that microclimate can cause 17% rise in cooling degree-day (CDD) and 7% reduction in heating degree-day (HDD) on average compared to mesoclimate. Considering typical weather conditions, CDD increases by 45% and HDD decreases by 8% from one 30-year period to another. Differences can become much larger during extreme weather conditions. For example, CDD can increase by 500% in an extreme warm July compared to a typical one. Results also indicate that annual cooling demand becomes four and five times bigger than 2010-2039 in 2040-2069 and 2070-2099, respectively. The daily peak cooling load can increase up to 25% in an extreme warm day when accounting for microclimate. In the absence of cooling systems during extreme warm days, the indoor temperature stays above 26°C continuously over a week and reaches above 29.2°C. Moreover, the annual overheating hours can increase up to 140% in the future. These all indicate that not accounting for influencing climate variations can result in maladaptation or insufficient adaptation of urban areas to climate change.
... Despite limitations such as model complexity, lack of extensive data, high computation time and methodological uncertainties [12], Building Stock Modelling (BSM) has commonly been used to assess large-scale building energy performance. According to Swan and Ugursal [13] building stock modelling approaches can be classified into two groups: top-down or bottom-up. ...
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Due to 2030 and 2050 targets of the latest international standards, energetically retrofitting the existing building sector requires special attention. Prior to the proposal of retrofit strategies, it is necessary to analyse the current energy performance of the stock. Although simulation tools provide accurate results of energy performance at the building level, individual assessments of the large-scale stock lead to extensive data collection and huge computational resources. This paper assesses the current performance of one of the most representative building typologies in social housing stock in southern Spain, the H-typology, predicting results on indoor thermal comfort at the stock level. The physical, constructive and geometrical building characterisation and the selection of a calibrated and validated case study through monitoring are used to generate parameterized energy simulation models, providing statistically representative samples of the stock. Open-access energy simulation tools have been combined with statistical software. Conclusions reported show average annual discomfort hours of around 68%, with higher percentage of annual undercooling discomfort hours, and identify the most influential parameters on indoor thermal comfort as infiltration rate, people density and night-time natural ventilation rate. Moreover, 10 Latin Hypercube Samples per parameterized variable derived in highly representative results for thermally analysing the stock.
... While the crowd sourced nature of OSM has been a key to its success, the small number of volunteers with professional GIS experience has raised significant concerns about its accuracy. Several studies [19,20] have performed an assessment of OSM spatial accuracy and completeness of building footprints. However, because all these studies have focused their analysis on very limited geographical regions, it is not reliable enough to extrapolate a general conclusion regarding the quality of OSM in terms of building footprint. ...
Article
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Advances in machine learning and computer vision, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics, cost-effectively, and at scale. These characteristics are relevant to a variety of urban and energy applications, yet are time consuming and costly to acquire with today’s manual methods. Several recent research studies have shown that in comparison to more traditional methods that are based on features engineering approach, an end-to-end learning approach based on deep learning algorithms significantly improved the accuracy of automatic building footprint extraction from remote sensing images. However, these studies used limited benchmark datasets that have been carefully curated and labeled. How the accuracy of these deep learning-based approach holds when using less curated training data has not received enough attention. The aim of this work is to leverage the openly available data to automatically generate a larger training dataset with more variability in term of regions and type of cities, which can be used to build more accurate deep learning models. In contrast to most benchmark datasets, the gathered data have not been manually curated. Thus, the training dataset is not perfectly clean in terms of remote sensing images exactly matching the ground truth building’s foot-print. A workflow that includes data pre-processing, deep learning semantic segmentation modeling, and results post-processing is introduced and applied to a dataset that include remote sensing images from 15 cities and five counties from various region of the USA, which include 8,607,677 buildings. The accuracy of the proposed approach was measured on an out of sample testing dataset corresponding to 364,000 buildings from three USA cities. The results favorably compared to those obtained from Microsoft’s recently released US building footprint dataset.
... Under the design and construction zone, passive design strategies and bioclimatic design are amongst the main solutions to decrease energy demand (Mirrahimi et al., 2016). A passive building design, being directly related to energy use, can aid energy conservation efforts (Schiefelbein et al., 2019;Zhang et al., 2019). Studies classified building design factors into five parameters: namely, shape, transparent surface, orientation, thermal-physical properties of building materials and distance between buildings (Pacheco et al., 2012;Rodrigues et al., 2019). ...
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Malaysia is a nation that has undergone a massive development based on its abundance of fuel supply. The imbalance ratio between gross domestic products and energy demand clearly indicates the need to promote energy-efficiency strategies in the country. This study investigates the relationship between building shape and energy consumption by considering the control of excessive solar radiation in a tropical climate. In the first step, four basic plan geometries, namely, square, rectangle, triangle and circle shapes, are studied to determine the optimal building shape in terms of energy consumption in Penang, Malaysia. Results of simulation analysis using DesignBuilder software (Version 5.4.0) reveal that the circle is the most suitable form in terms of energy performance. In the second step, all buildings with extended shapes based on the optimal shape obtained from the first step are simulated under the same condition to analyse the thermal behaviour of different building forms. Amongst four alternative extended cases, Case 3 with 90 cm depth and without vertical offset from the top of the window has superior energy performance and sufficient natural daylight. This study contributes to enhance energy efficiency of new buildings by incorporating design strategies in the design process.
Article
This paper presents an innovative approach to addressing the prevalent challenge of simulation uncertainty in urban building energy modeling (UBEM), focusing on accurately determining occupant-related input parameters. Traditional UBEM methods typically rely on standard schedules to create archetype models, which often fail to reflect the variability observed in real-world scenarios. To overcome this limitation, this research introduces a novel framework for generating electricity use profiles in institutional building archetypes across various climate zones. This framework integrates k-means clustering with Gaussian processes, effectively incorporating uncertainties into the prediction models. The evaluation of this stochastic model suggests that the methodology can give acceptable predictions on the electricity consumption of institutional buildings. The model demonstrates robust predictive capabilities, achieving a CVRMSE as low as 11% on weekdays and 8.7% on weekends, reflecting its strong predictive performance. However, its performance varies among different clusters and time periods, with specific clusters displaying more significant predictive inaccuracies at particular times. These results emphasize the importance of fine-tuning models and offer opportunities for improvement in predicting urban building energy consumption. This can be achieved by incorporating sensor-derived data to develop more detailed building profiles that include variable electricity usage patterns. This methodology has been integrated into a UBEM tool, enabling the generation of more realistic electricity load profiles.
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With buildings accounting for 40% of global carbon emissions, cities striving to meet sustainability targets aligned with the Paris Agreement must retrofit their existing building stock within 30 years. Previous studies have shown that urban building energy models (UBEMs) can help cities identify technology pathways — combinations of energy efficiency retrofits and renewable energy deployment strategies — to meet emissions reduction goals. UBEMs are currently limited by cost to only the largest cities but must be expanded to all cities if society is going to meet scientifically-identified emissions reduction goals. This manuscript presents an eight-step framework to scale technology pathways analyses using UBEMs to all communities in a repeatable, affordable manner. The roles and responsibilities of three key personas, the sustainability champion, GIS manager, and an energy modeler, for each step are identified. The eight-step process is tested with a case study of 13,100 buildings in Oshkosh, WI, USA. The case study identified a technically-feasible path to nearly net zero emissions for Oshkosh’s buildings. Constraints in the workforce, supply chain, and retrofit adoption to attain this goal were identified to inform policymakers. The case study suggests that the eight-step process is a blueprint for action in communities around the world.
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This work introduces a decentralized management concept for the urban charging hubs (UCHs) where electric vehicles (EVs) can access multiple charger clusters, each controlled by an aggregator. The given day ahead schedules (DASs) and peak power limits (PPLs) of the aggregators providing grid-to-vehicle (G2V) and vehicle-to-grid (V2G) services can constrain the energy supply. A suitable energy management concept is required to prevent the impact of supply limitations on EV users. In the proposed approach, an electromobility operator (EMO) acting as an authorized entity, allocates incoming EVs into the charger clusters in the UCH. The EMO executes a smart routing (SR) algorithm that jointly optimizes the cluster allocations and charging schedules, minimizing the charging cost for the given dynamic price signals produced by the aggregators. For real-time charging control (RTC) of the charging units, each aggregator solves an optimization problem with periodically updated parameters given by the DAS/PPLs and charging commitments. This work demonstrates the effectiveness of the proposed concept through comparisons against benchmark strategies without SR and RTC. The results highlight that the proposed concept reduces the deviations from the DASs and the violations of PPLs while significantly decreasing unfulfilled charging demand and unscheduled discharge from EV batteries.
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The existing building patrimony is responsible for 36% of the global energy use and 37% of the greenhouse gas emissions. It is hence a major challenge to improve its energy performance. According to the Renovation Wave, the average annual renovation rate should be doubled by 2030 up to 3% and deep energy renovations should be encouraged. The Belgian city of Leuven works towards this target and is even more ambitious, setting their goal on becoming climate neutral by 2050. The strategy investigated in this study is to increase the renovation rate by clustering renovations, which is challenging since the Belgian building stock is highly privatised. Based on a thorough literature study, this paper examines various methodologies for building stock modelling. The main focus is comparing the required input data with the data availability, handling the data gaps, and defining their influence on the model’s accuracy. The findings are applied to Leuven by analysing the main drivers to cluster renovation measures. However, many data gaps appeared, leading to the selection of a GIS-enhanced archetype model enriched by energy data as the most suitable approach. To avoid misinterpretation due to differences in data quality, transparent reporting in stock modelling is recommended.
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Dynamic urban simulations often face three main challenges: 3D digital city generations, building archetype creations, and inclusions of urban microclimate impacts due to limited data and computing resources available. This study introduces a new approach for the 3D city generation by integrating publicly available data sets (OpenStreetMap and Microsoft footprints) and a free program (Google Earth). These data sets provide 2D building footprints, whereas Google Earth provides digital surface models of terrains and buildings. The building archetype library of non-geometrical properties was created based on building types and years of constructions in the form of shapefiles joined with the 3D city data through QGIS. The proposed workflow also includes the dynamic integration of urban microclimate (CityFFD) and building thermal/energy models (CityBEM). The dynamic simulations were achieved using weather station data as boundary conditions, including air temperature, solar radiation, and wind speed and direction, instead of typical meteorological year data. The transient microclimate results were validated using local weather station data, and dynamic energy simulation results were validated using measured power consumption data. The study provides a solution to dynamic urban building energy and microclimate modeling by publicly available data sets and tools.
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Climate change, increasing emissions and rising global temperatures have gradually affected the way we think about the future of our planet. Urban areas possess significant potential for reducing the energy consumption of the overall energy system. In recent years, there is an increasing number of research initiatives related to Urban Building Energy Modelling (UBEM) that focus on simulation processes and validation techniques. Although input data are crucial for the modelling process as well as for the validity of the results, the availability of input data and associated data formats were not analysed in detail. This paper closes the identified knowledge gap by presenting a taxonomic analysis of key UBEM components including: input data formats, simulation tools, simulation results and validation techniques. This paper concludes that over ∼95% of the studies analysed were not reproducible due to the absence of information relating to key aspects of the respective methodologies such as data sources and simulation workflows. This paper also qualifies how weak levels of interoperability, with respect to input and output data, is present in all phases of UBEM.
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A traffic assignment model is a critical tool for developing future transport systems, road policies, and evaluating future network upgrades. However, the development of the network and demand data is often highly intensive, which limits the number of cases where some form of the models are available on a global basis. These problems include licensing restrictions, bureaucracy, privacy, data availability, data quality, costs, transparency, and transferability. This paper introduces Rapidex, a novel origin–destination (OD) demand estimation and visualisation tool. Firstly, Rapidex enables the user to download and visualise road networks for any city using a capacity-based modification of OpenStreetMap. Secondly, the tool creates traffic analysis zones and centroids, as per the user-specified inputs. Next, it enables the fetching of travel time data from pervasive traffic data providers, such as TomTom and Google. With Rapidex, we tailor the genetic-algorithm (GA)-based metaheuristic approach to derive the OD demand pattern. The tool produces critical outputs such as link volumes, link travel times, OD travel times, average trip length and duration, and congestion level, which can also be used for validation. Finally, Rapidex enables the user to perform scenario evaluation, where changes to the network and/or demand data can be made and the subsequent impacts on performance metrics can be identified. In this article, we demonstrate the applicability of Rapidex on the network of Sydney, which has 15,646 directional links, 8708 nodes, and 178 zones. Further, the model was validated using the Household Travel Survey data of Sydney using the aggregated metrics and a novel project selection method. We observed that 88% of the time, the “estimated” and “observed” OD matrices identified the same project (i.e., the rapid process estimated the more intensive traditional approach in 88% of cases). This tool would help practitioners in rapid decision making for strategic long-term planning. Further, the tool would provide an opportunity for developing countries to better manage traffic congestion, as cities in these countries are prone to severe congestion and rapid urbanisation while often lacking the traditional models entirely.
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Modeling and optimization of mixed use areas. This article deals with the development of a planning tool for mixed use areas within the project ”En-Eff:Stadt – Bottrop, Welheimer Mark“. The city district Welheimer Mark is a mixed use area within the InnovationCity Ruhr of Bottrop, Germany. The main aim was the energetic optimization of the city district to reduce greenhouse gas emissions. Thus, methods for complex city district modeling has been developed and used within Welheimer Mark district. Simulated thermal and electrical demands only showed a difference of 6.4 % related to energy consumption values. While a sufficient method for generation of electrical load profiles of non-residential buildings could not be identified, a method for generation of thermal load profiles shows a good fit between generated and measured loads. An optimization model has been used to identify an optimized energy system distribution. A greenhouse gas emission reduction up to 50 % is possible at cost increase of 6 % to 11 %. Copyright © 2017 Ernst & Sohn Verlag für Architektur und technische Wissenschaften GmbH & Co. KG, Berlin http://onlinelibrary.wiley.com/doi/10.1002/bapi.201710001/epdf