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One widely recognized opportunity to reduce global carbon emissions is to make urban neighborhoods more resource efficient. Significant effort has hence gone into developing computer-based design tools to ensure that individual buildings use less energy. While these tools are increasingly used in practice, they currently do not allow design teams t...
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... from left to right users initially select a site location plus other site conditions including the amenity template that is used for walkability evaluations (see below). Umi users are then provided with an intuitive layering structure in Rhinoceros to build massing models of a city or neighborhood that consist of building envelopes, trees, shading objects, other infrastructure, and streets ( Figure 2). Buildings, trees and all kinds of shading objects are represented as closed polysurfaces. ...
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... mentioned before, energy simulation results can be mapped back into the Rhinoceros scene (Figure 3) and combined with aggregate analysis and visualizations of building performance. As an example, Figure 4 shows aggregate hourly load curves for electricity gas and associated carbon emissions using mean conversion factors from ASHRAE 189.1 (2010) for the neighborhood from Figure 2.There are some pronounced heating demand peaks in January and early December which could be potentially mitigated via architectural interventions and lead to substantial equipment savings if the neighborhood were to be served by a district heating and cooling system. ...
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... corresponds to daylight autonomy with the exception that partial credit is given when daylight meets only parts of the target level at a given time step. Figure 5 shows the continuous daylight autonomy distribution in the mixed use neighborhood form Figure 2 assuming illuminance thresholds of 300 lux and 500 lux for residential and commercial buildings, respectively. For the overall neighborhood, 45% of the floor area has a CDA over 50%. ...
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... contrast only 14% have a DA over 50% which the IESNA LM-83-12 would consider 'daylit' (IESNA 2012). Figure 6 shows an outdoor thermal comfort analysis of the mixed use neighborhood form Figure 2 using Umi-Daylight. In this case a simplified model is used which considers an outdoor space to be 'cold' if the outdoor ambient temperature is below 5 o C and no direct radiation is incident on an outdoor location. ...
Citations
... The data was then combined with Future Typical Meteorological weather data developed by [26]. Subsequently, this UHI-induced weather data, were incorporated into the Urban Modelling Interface (umi) developed by [27] to conduct an in-depth energy simulation of urban buildings. ...
Urban areas often experience higher air temperatures than their surrounding rural counterparts, a phenomenon known as the urban heat island (UHI) effect. This significant human-induced alteration of urban microclimates has notable consequences, especially on urban energy consumption and resulting economic implications. This study presents an in-depth analysis of the UHI effect on urban building energy consumption in a US Midwest neighbourhood. Utilizing a three-phase methodology, the research first simulated UHI intensities with current and future Typical Meteorological Year (TMY) data, integrated with the Local Climate Zone (LCZ) classification system and the Urban Weather Generator (UWG) model. The second phase employed the urban modelling interface (umi) for building energy simulation, capturing the UHI impact on both residential and commercial buildings. The third phase demonstrates that UHI effects lead to reduced heating demand but increased cooling requirements in the future, with residential areas being more affected. The study's findings reveal critical challenges for urban planners and policymakers, emphasizing the need for sustainable designs to address fluctuating heating and cooling demands in changing climates.
... Other factors such as the experience of the user or the availability of the program, especially if it is an open-access software, influence the choice of software. Some of the tools that can be used for urban simulation are the following: CityBES [68], City Energy Analyst (CEA) [69], CitySim [70], UMI [71], and URBANopt [72]. The main characteristics of the existing software tools are indicated in Table 2. Table 2. Software tools to perform Urban Building Energy simulations. ...
... Reinhart et al. [71] presented in 2013 a Rhinoceros-based urban modelling design tool to perform evaluations of operational energy consumption, daylighting, and walkability, called UMI. It uses Rhinoceros as the CAD platform to build the 3D geometric model, Energy Plus to perform building energy simulations, Daysim for daylight simulations, and, also, custom Python scripts that allow the assessment of walkability. ...
Building integrated photovoltaics (BIPV) consists of PV panels that are integrated into the building as part of the construction. This technology has advantages such as the production of electricity without necessary additional land area. This paper provides a literature review about the recent developments in urban building energy modelling, including tools and methods, and how they can be used to predict the effect of PV systems on building outdoor and indoor environments. It is also intended to provide a critical analysis on how PV systems affect the urban environment, both from an energy and a comfort point of view. The microclimate, namelly the urban heat island concept, is introduced and related with the existence of PV systems. It is concluded that urban building energy modelling (UBEM) can be effective to study the performance of PV systems in the urban environment. It allows to simultaneously predict the building energy performance and the microclimate effect. However, there is a need to develop new methodologies to overcome the challenges associated to UBEM, especially in what concerns non-geometric data, which leads to a major source of errors. and to find an effective method to predict the effect of PV systems in the urban environment.
... Other factors such as the experience of the user or the availability of the program, especially if it is an open-access software, influence the choice of software. Some of the tools that can be used for urban simulation are the following: CityBES [68], City Energy Analyst (CEA) [69], CitySim [70], UMI [71], and URBANopt [72]. The main characteristics of the existing software tools are indicated in Table 2. [68]. ...
... Reinhart et al. [71] presented in 2013 a Rhinoceros-based urban modelling design tool to perform evaluations of operational energy consumption, daylighting, and walkability, called UMI. It uses Rhinoceros as the CAD platform to build the 3D geometric model, Energy Plus to perform building energy simulations, Daysim for daylight simulations, and, also, custom Python scripts that allow the assessment of walkability. ...
Building integrated photovoltaics (BIPV) consists of PV panels that are integrated into the building as part of the construction. This technology has advantages such as the production of electricity without necessary additional land area. This paper provides a literature review about the recent developments in urban building energy modelling, including tools and methods, and the effect of PV systems on building outdoor and indoor environments. It is also intended to provide a critical analysis on how PV systems affect the urban environment, both from an energy and a comfort point of view. The urban heat island development is introduced and related with the existence of PV systems. It is concluded that data acquisition is still a weakness in urban building energy modelling, especially in what concerns non-geometric data, which leads to a major source of errors. The availability of data is a challenge because frequently they are not accessible, due to privacy, high-cost or other reasons. Also, methodologies to develop precise archetypes for creating urban building energy models are lacking.
... Thus, to appropriately represent such a context, a UBEM tool would need to consider the effect of multi-zone airflows, as well as incorporate inputs describing building operation concerning ventilative cooling or other passive strategies. Considering that many of these tools (e.g., urban modeling interface [UMI], CityBES, and URBANopt (El Kontar et al., 2020;Hong et al., 2016;Houssainy et al., 2020;Reinhart et al., 2013)) rely on EnergyPlus (which already offers this functionality) as the simulation engine, adaptations are theoretically possible. However, it is uncertain whether an adequate simulation of airflows would be obtained in overly simplified building zones (e.g., UMI's Shoeboxer (Dogan & Reinhart, 2017)). ...
In response to increasingly severe weather conditions, optimization of building performance and investment provides an opportunity to consider co-benefits of thermal resilience during energy efficiency retrofits. This work aims to assess thermal resilience of buildings using building performance simulation to evaluate the indoor
overheating risk under nine weather scenarios, considering historical (2010s), mid-term future (2050s), and long-term future (2090s) typical meteorological years, and heat wave years. Such an analysis is based on resilience profiles that combine six integrated indicators. A case study with a district of 92 buildings in Brazil was
conducted, and a combination of strategies to improve thermal resilience was identified. Results reflect the necessity of planning for resilience in the context of climate change. This is because strategies recommended under current conditions might not be ideal in the future. Therefore, an adaptable design should be prioritized.
Cooling energy consumption could increase by 48 % by the 2050s, while excessive overheating issues could reach 37 % of the buildings. Simple passive strategies can significantly reduce the heat stress. A comprehensive thermal resilience analysis should ultimately be accompanied by a thorough reflection on the stakeholders’
objectives, available resources, and planning horizon, as well as the risks assumed for not being resilient.
... Urban building energy modelling (UBEM) has emerged in recent years, enabled by the uptake of smart metering and the growing availability of data and computation capacity [1]. Various UBEM environments have been developed using either detailed physics-based thermal engines such as EnergyPlus [2][3][4], simpler reduced-order models based on self-made RC networks [5][6][7], energy signatures [8] or the ISO/CEN standard method [9]. The purpose of many UBEMs is to identify building types within a district or large urban area with the greatest potential for energy savings, and to determine which energy conservation measures (ECMs) could result in the best investments choices for major policy decisions. ...
Urban Building Energy modelling (UBEM) has emerged in the last decade as an important tool to accelerate
energy transition in the building sector. To fulfil one of its major purposes - forecasting energy savings for potential energy conservation measures at an urban scale, several challenges are still to be addressed. Two key
challenges include: 1) the need to calibrate models with a large number of unknown parameters across numerous
buildings (either individually or through archetype representation); 2) the limited availability of high-resolution
measured data, which raises concerns about calibrated models based solely on yearly values for accurate energy
savings forecast. This study addresses these challenges using a case study of 35 buildings in a district in
Stockholm, Sweden. Firstly, a new iterative Approximate Bayesian Computation (ABC) method for calibration is
proposed, incorporating a copula-based sampling process at each iteration. Secondly, the new calibration process
is applied with three different time resolutions to examine the impact of data resolution on forecasted energy
usage, particularly when applied to a classical energy conservation measure. Results demonstrate the efficiency
of the new method across the 35 buildings, facilitating the rapid population of a final joint distribution for the
nine unknown parameters considered in each building. While the marginals may be strongly influenced by the
time resolution, the forecasted energy consumption remains identical across the three analysed time resolutions.
However, a noticeable difference is observed when the ECM pertains to a formerly unknown or known
parameter.
... Urban Building Energy Modelling is an efficient, low-cost, and scalable workflow that can be used to model the energy performance of buildings in urban areas [13]. UMI is a "multi-scale, multi-physics platform that allows users to simulate and visualize the performance of buildings and urban areas at various scales [14]. Rapid urbanization is taking place in developing countries at an unprecedented rate, with the majority of the world's population projected to live in urban areas by 2050 [15]. ...
Building construction accounted for more than 40% of global energy consumption and 30% of greenhouse gas emissions. It is essential to understand embodied carbon in buildings at the neighbourhood level, as after construction, it will be locked there for several years. This study aims to analyze the embodied energy (EE) of the neighbourhood for redevelopment scenarios using (UBEM). The study area chosen was Rasta Peth, Pune. The primary sources of embodied energy were discovered by analyzing existing dense neighbourhoods. Massing cases for redevelopment scenarios were created according to UDCPR guidelines. A comparative analysis of EE between the redeveloped 2030 scenarios was conducted. The research results show that the low carbon 2030 scenario has 27.9% lower EE than the conventional scenario. The findings emphasized the importance of embodied energy in sustainability strategies for urban planners and policymakers. This research contributed valuable insights for reducing embodied energy in urban areas.
... umi (Reinhart et al., 2013) is a Rhinoceros-based urban modeling tool that allows for comprehensive operational energy, daylighting, and walkability assessments of entire neighborhoods, utilizing simulation engines like EnergyPlus and Radiance/Daysim, along with Grasshopper and Python scripts. CitySim (Robinson et al., 2009) focuses on calculating heating and cooling demands; SimStadt, primarily employed for rapidly generating evaluation scenarios to assess city-scale heating requirements; City Energy Analyst (Fonseca et al., 2016), a Python-driven tool with an intuitive graphical interface, streamlines analysis of building heating and cooling loads for district energy planning. ...
... Hong et al., 2016). URBANopt by the National Renewable Energy Laboratory (El Kontar et al., 2020), UMI by the Massachusetts Institute of Technology (Reinhart et al., 2013), and CESAR by the Swiss Federal Institute of Technology Zurich(D. Wang et al., 2018), all of which utilize EnergyPlus at their core. ...
... A particularly notable center for research and development in this field is the Sustainable Design Lab at MIT, where researchers developed the "Shoeboxer" algorithm to simplify the simulation of building sets based on an analysis and discretization of the building mass in a city sector [5]. This led to the development of the energy consumption module of the Urban Modeling Interface (UMI) tool, based on the EnergyPlus calculation engine [6]. Among the earlier review articles, a notable primary contribution is that by Reinhart and Cerezo-Davila (2016) [4]. ...
... For urban-scale energy modeling and calculation, the UMI tool, which is highly scientifically valid [6], based on an EnergyPlus calculation engine, was used. The Shoeboxer algorithm was used to apply it to a large set of buildings [5], while UBEM.io was used to accelerate the semi-automatic modeling process [7]. ...
The study of energy consumption in buildings, particularly residential ones, brings with it significant socio-economic and environmental implications, as it accounts for approximately 40% of CO2 emissions, 18% in the case of residential buildings, in Europe. On a number of levels, energy consumption serves as a key parameter in urban sustainability indicators and energy plans. Access to data on energy consumption is crucial for energy planning, management, knowledge generation, and awareness. Urban Building Energy Models (UBEMs), which are emerging tools for simulating energy consumption at neighborhood scale, allow for more efficient intervention and energy rehabilitation planning. However, UBEM validation requires reliable reference data, which are often challenging to obtain at urban scale due to privacy concerns and data accessibility issues. Recent advances, such as automation and open data utilization, are proving promising in addressing these challenges. This study aims to provide a standardized UBEM validation process by presenting a case study that was carried out utilizing open data to develop bottom-up engineering models of residential energy demand at urban scale, with a resolution level of individual buildings, and a subsequent adjustment and validation using reference tools. This study confirms that the validated GIS-UBEM model heating and cooling demands and consumption fall within the confidence bands of ±15% and ±12.5%, i.e., the confidence bands required for the approval of official alternative simulation methods for energy certification. This paves the way for its application in urban-scale studies and practices with a well-established margin of confidence, covering a wide range of building typologies, construction models, and climates comparable to those considered in the validation process. The primary application of this model is to determine the starting point and subsequent evaluation of improvement scenarios at a district scale, examining issues such as massive energy rehabilitation interventions, energy planning, demand analysis, vulnerability studies, etc.
... Evaluation of the conducted measures, i.e., use of LED lamps, improvement of the efficiency of heating and cooling units, adding an economizer to the ventilation system and replacing windows, showed a 22%-48% reduction in the energy demand of each building. In [8], the UBEM of Boston which was developed around an UBEM interface known as ''UMI'' [10] is also another example for planning and evaluation of large-scale energy retrofits. In this study, the influence of photovoltaics (PV) power generation on peak shaving for the electricity load was the basis for the comparison of two retrofit scenarios, one suggesting the installation of PV panels on 50% of the roof area of all buildings and the other adjusting the room temperature of commercial buildings using a demand-response control that allowed for an increase of 2-4 • C in the indoor temperature during summer time. ...
To evaluate the effects of different energy retrofit scenarios on the residential building sector, in this study, an urban building energy model (UBEM) was developed from open data, calibrated using energy performance certificates (EPCs), and validated against hourly electricity use measurement data. The calibrated and validated UBEM was used for implementing energy retrofit scenarios and improving the energy performance of the case study city of Varberg, Sweden. Additionally, possible consequences of the scenarios on the electricity grid were also evaluated in this study. The results showed that for a calibrated UBEM, the MAPE of the simulated versus delivered energy to the buildings was 26 %. Although the model was calibrated based on annual values from some of the buildings with EPCs, the validation ensured that it could produce reliable results for different spatial and temporal levels than calibrated for. Furthermore, the validation proved that the spatial aggregation over the city and temporal aggregation over the year could considerably improve the results. The implementation of the energy retrofit scenarios using the calibrated and validated UBEM resulted in a 43 % reduction of the energy use in residential buildings renovated based on the Passive House standard. If this was combined with the generation of on-site solar energy, except for the densely populated areas of the city, it was possible to reach near zero (and in some cases positive) energy districts. The results of grid simulation and power flow analysis for a chosen low-voltage distribution network indicated that energy retrofitting of buildings could lead to an increase in voltage by a maximum of 7 %. This particularly suggests that there is a possibility of occasional overvoltages when the generation and use of electricity are not in perfect balance.
... 127 The modeling approach is 3D graphics-based geometry since UMI is a tool based on Rhino 3D. 128 Trees and waterbodies can be considered inside UrbaWind, provided they are included as STL files and uploaded inside the widow POROSITY. 129 Only the porosity of the material is taken into account. ...
The impact of human activities on climate change has become increasingly evident, with cities being particularly vulnerable to its effects. Anthropogenic emissions, such as heat and greenhouse gases, are projected to intensify climate-induced phenomena, which can lead to negative health outcomes. To understand how human health would be affected by such climate-exacerbated phenomena, computational models that consider the local microclimate are essential to better regulate cities to respond to these phenomena. Many simulation tools have been created and enhanced over the years. Therefore, this study systematically reviews the currently available urban microclimate simulation tools and compares their features and capabilities. The review suggests that these models can effectively assist in investigating urban health and testing adaptation strategies, but it is important to acknowledge their limitations due to assumptions made. Nonetheless, with proper interpretation and utilization, these models can provide valuable insights and contribute to informed decision-making processes.
