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• A pipeline is formulated to extract window to wall ratios from building façades. • A grammar based and learning based method are compared for performance efficacy. • The learning-based method shows higher accuracy than the grammar-based method. • A hybrid method combining the strengths of the methods achieves higher accuracy. • An algorithm utilizing the learning-based method extracts detailed building heights. Urban feature extraction methods have presented multiple opportunities in the field of environmental building performance studies, and urban assessments. With reference to façade features, window size and position are key parameters that influence occupant perception and environmental performance of indoor spaces. A myriad of methods have been proposed to automatically detect façade window layouts from street view images yet it is still unclear how to assess the strengths and limitations of these methods or how they can be combined for higher detection performance. This paper aims to add clarity for those aiming to use computer vision techniques for façade layout extraction for urban level sustainability assessments. An automated pipeline to enable the extraction and detection of WWRs (Window to Wall ratios) is introduced that is based on two fundamentally different computational approaches; a grammar-based edge detection framework (Method 1) and a learning-based method (Method 2) that utilizes CNNs (Convolutional Neural Networks). The paper then compares the detection efficacy of both methods, focusing on WWR accuracy, in New York and Lisbon, including their ability to extract more detailed façade properties such as floor-to-floor heights. The study finds that the learning-based method shows lower error scores across the two cities. In Lisbon, 69 % of conditions were detected within the 10 % error range and 91 % were within the 20 % range under Method 1. Under Method 2, 82.5 % of conditions were within the 10 % error range and 95 % were within the 20 % range. In New York, 66 % of conditions were within the 10 % error range and 90 % were within the 20 % range under Method 1 while 77.5 % of conditions were within the 10 % error range and 93 % were within the 20 % range under Method 2. Finally, a hybrid method is proposed to leverage the strengths of the two models, and higher accuracies are obtained in both the New York and Lisbon dataset. In New York, 96.5 % are now detected within the 20 % error range and 81.5 % within the 10 % error range. In Lisbon, 96 % are now detected within the 20 % error range and 83.5 % within the 10 % error range. With reference to the total building height extraction formulated under Method 2, the results show a relative error of 3.5 % in height estimation in the sample set. [ABSTRACT FROM AUTHOR]

Urban building energy modeling (UBEM) and its associated tools have proliferated in recent years, leading to various model development, simulation, calibration approaches, and use cases. UBEM is becoming a valid policy support tool to guide planning and intervention efforts at the neighborhood and city scale. However, current UBEM workflows focus primarily on the physical properties of buildings and geospatial geometry data without consideration for socioeconomic factors or demographic characteristics of the modeled/studied areas. This limitation impedes UBEM's effectiveness as a policy support tool. This paper presents a novel method – using a combination of supervised and unsupervised learning techniques on smart meter and census data – to develop hourly usage schedules for different socioeconomic personas. The schedules can be used to refine building archetypes for UBEMs. The method is piloted in two cities with similar building stocks but different socioeconomic compositions. It was found that socioeconomic indicators are important in classifying building archetypes. Considering these indicators changes the predicted city-wide energy use for residential buildings by up to 10%. The method presented is scalable and applicable to cities and municipalities worldwide (large or small) and elucidates the importance of accounting for demographic and socioeconomic indicators to reflect lived realities accurately. • A novel method to incorporate socioeconomic indicators in urban building energy modeling was developed. • Persona archetypes for UBEMs were created using smart meter data. • The method combined supervised and unsupervised learning techniques. • Simulation results were found to vary by approximately 10 %. • Feature importance could further identify important variables in urban building energy use. [ABSTRACT FROM AUTHOR]

The outdoor microclimate highly affects the quality of urban outdoor spaces which are essential to a city's socio-economic vitality. Due to the complexity associated with estimating outdoor thermal comfort (OTC), its study is currently only feasible at a high computational cost and time which makes it ineffective in any iterative design process. By coupling physics-based simulations and statistical modelling techniques, this paper presents a probabilistic, progressive, and accuracy-adaptive modelling approach for faster spatially-resolved OTC estimation that is scalable to large urban neighborhoods. This is achieved through three interrelated strategies: (1) Throughout the different simulation stages, spatiotemporal OTC categories are displayed with successively rising confidence levels to support instant design decision-making. (2) Confidence levels are based on probability distributions of partially known environmental variables such as wind or mean radiant temperature. (3) Wind distributions across an urban area are initially based on a spatially-informed set of rules and later replaced with explicit simulations of wind flow fields. The approach is tested against state-of-the-art computational fluid dynamic simulations for a 3 km2 sample area of San Francisco's financial district. Results show that scalable and actionable predictions are achievable at all simulation stages with the percentage of misclassified hourly OTC ranges during occupied hours falling from 36% for instant climate-based results to 8% and 7% for spatially clustered wind and building aerodynamics informed predictions which take minutes to calculate for the investigated urban area. The building aerodynamics informed simulations accurately predicts diurnal and seasonal OTC ranges for, on average, 97% of outdoor points. • A novel probabilistic, progressive, and accuracy-adaptive framework is presented. • Faster, scalable, and actionable outdoor thermal comfort predictions are achievable. • Integrating spatially informed wind distributions enhance accuracy. • Fit-for-purpose modelling enables abstractions at reduced error trade-off. • Spatially resolved wind flow insights are captured through flow aggregation strategy. [ABSTRACT FROM AUTHOR]

Abstract In a previous paper, we presented a novel approach to validate the capability of the biometeorological index, Universal Thermal Climate Index (UTCI), to predict the likelihood of urban dwellers to be outside in a public space for the heating dominated climate of Cambridge, MA. Occupancy patterns were recorded based on Wi-Fi data. The present study extends this approach to the hot and arid climate of United Arab Emirates (UAE) to evaluate the effect of outdoor evaporative coolers on resident presence in a public courtyard. Over a period of ten months, outdoor Wi-Fi access point data was collected in the public courtyard located on a university campus in Abu Dhabi. An analysis of the resulting MacID probes yields a population of 1200 regulars and 3800 visitors present in the courtyard at some point during the study period. Coincident UTCI simulations using ENVI-met strongly correlated with the number of regulars present during lunchtime both during times when the evaporative coolers were on (R2 = 75%) and off (R2 = 61%). Lunchtime attendance peaked for UTCI values in the thermal comfort range of around 24 °C during all seasons. The outdoor evaporative coolers were able to bring the UTCI down from very strong heat stress to between thermal comfort and moderate heat stress range. These findings confirm that UTCI can be used as a reliable environmental performance metric to support the design and preservation of comfortable outdoor spaces both in a hot and a cold climate, across a variety of cultural settings. Highlights • Wi-Fi data used to study the dwelling patterns of 5,000 individuals in outdoor public spaces in Masdar Institute, Abu Dhabi. • Lunchbreak patterns of 1,200 regulars correlated with Universal Thermal Climate Index (UTCI) simulations. • The outdoor evaporative coolers are effective for thermal comfort in the hot and arid climate of Abu Dhabi. • UTCI is a reliable performance metric for the design of outdoor spaces in both a hot and a cold climate. [ABSTRACT FROM AUTHOR]

To reduce global greenhouse gas emissions associated with building energy use, owners of large building portfolios such as university campuses frequently rely on building energy models (BEM) to better understand potential costs and benefits of retrofits. Model development workflows that are designed for individual buildings require a level of effort that would be time and cost prohibitive to apply to such campuses which often include hundreds of diverse-use buildings. While smaller campuses can effectively utilize the traditional BEM approach to study retrofit scenarios, this option is therefore not feasible for larger campuses. Large universities have instead utilized a combination of statistical and spreadsheet models which may not fully capture the unique architectural features, programmatic requirements, and systems configurations of individual campus buildings. With the goal of overcoming these limitations, two separate urban energy models, that employed considerably different methodologies, were developed for the campus of the Massachusetts Institute of Technology to evaluate future energy scenarios with an appreciably smaller effort vis-à-vis developing building-by-building energy models. This study reviews these two models with regards to their setup and calibration effort, ability to model individual building energy use, and the accuracy in predicted savings from implementing a variety of retrofitting measures. The paper identifies both models’ strengths and limitations and suggests best practice procedures for administrators of other campuses interested in undergoing a similar exercise. [ABSTRACT FROM AUTHOR]

Thermal comfort preferences of occupants and their interactions with building systems are top influential factors of residential space heating demand. Consequently, housing stock models are sensitive to assumptions made on heating temperatures. This study proposes a heat balance approach, inspired by the classical degree-day method, applied to an extensive urban dataset. The goal of this analysis is to determine heterogeneous characteristics, such as temperature setpoints of heating systems and thermal envelope characteristics from an overall population of residential buildings. Measured energy data are utilized for the purpose of the study from the city of Aarhus, Denmark, where the energy usage for heating of circa 14,000 households was monitored over time via smart meters. These data are combined with actual weather data as well as data extracted by a national building database. Using linear regression and heat balance models, temperature setpoints for the whole dataset are determined with a median and average of 19 °C and 19.1 °C, respectively. Furthermore, building related characteristics such as thermal and ventilation losses per building and overall heat transfer coefficients are extracted at urban scale. The reliability of the method over its complexity is discussed with regards to the big sample that has been applied to. In general, the overall performance of the approach is satisfactory achieving a coefficient of determination with an average of 0.8, and is found to be in line with previous findings, considering also the high uncertainty associated with building-related input parameters. The extracted setpoint distribution should be transferrable across Scandinavia. [ABSTRACT FROM AUTHOR]

This study presents a novel approach to validate the capability of biometeorological indices to predict the likelihood of urban dwellers to be outside during midday. Over a period of ten months three Wi-Fi scanners were used in a public courtyard in Cambridge, MA, to record outside dwelling patterns. Based on encrypted MacIDs courtyard attendees could be divided into 16,000 regulars and 676,000 visitors. Universal Thermal Climate Index (UTCI) predictions based on a combination of measured microclimatic conditions and mean radiant temperature simulations using ENVI-met were shown to strongly correlate with the number of regulars present during lunchtime with coefficients of determination (R 2 ) of 92% during spring and 70% during summer/fall, respectively. Lunchtime attendance peaked for UTCI values in the thermal comfort and moderate heat stress ranges. In parallel, the probability for regulars to have lunch outside more than doubled during those UTCI conditions and the median lunchbreak length increased from 8 min to 12 min. These findings suggest that UTCI can be used as a reliable environmental performance metric to support the design and preservation of comfortable outdoor spaces. The reported use of public Wi-Fi data can help city governments to better understand – and potentially improve – the use of outdoor spaces while maintaining the privacy of their constituents. [ABSTRACT FROM AUTHOR]

Providing healthy food for the world’s growing urban population is a recognized global challenge and it is likely that current modes of conventional, large-scale farming will over time be increasingly complemented by local, urban farming practices. Apart from its acknowledged social benefits, urban farming is also widely viewed as a more resource-efficient alternative to conventional remote farming. Especially indoor, soilless cultivation in urban areas is being portrayed as a particularly sustainable solution. However, as this technique relies on controlled environments, its ongoing operation can be quite energy-intensive and related carbon emissions should be carefully weighed against reduced emissions, such as those from transportation. To further this goal, this article presents a simulation-based environmental analysis workflow for Building-Integrated Agriculture (BIA) in urban contexts, that includes detailed solar radiation, water and energy specific models. The aim of the workflow is to guide the user through decision-making on the potentialities of implementing BIA in a given neighborhood while maximizing crop yields and minimizing water and energy consumption. The workflow was applied to three hi-tech urban farming scenarios in Lisbon, Portugal: a polycarbonate Rooftop Greenhouse (RG), a Vertical Farm (VF) with windows and skylights on the top floor of a reinforced-concrete building as well as a completely opaque VF with no penetration of natural light on the ground floor of a reinforced-concrete building. Global Warming Potential (GWP) related to water, transportation and operational energy of these three case studies were compared to GWP of (i) the currently existing supply chain for tomato, and (ii) a hypothetical low-tech unconditioned rooftop urban farm. Results show that the RG and the top floor VF had the best overall environmental performance, respectively cutting greenhouse gas emissions in half and in three in comparison with the existing supply chain for tomato. By allowing this preliminary assessment of alternative farm locations and properties, the workflow provides the user with actionable information for early-stage holistic assessment of BIA projects. [ABSTRACT FROM AUTHOR]

The necessary simplification of building stock models to perform energy studies in cities can impact their accuracy. This paper assesses the impact of considering different levels of detail in the characterization of the building stock, by varying the number of archetypes considered, using a neighborhood in Lisbon as case-study. Each archetype represents a share of buildings and is defined by the most representative construction parameters and average geometrical characteristics. The results show that while oversimplification might lead to large differences, there is also no need to consider a very detailed characterization of the building stock to obtained consistent results. [ABSTRACT FROM AUTHOR]

Image-based visual discomfort analysis has strong potential to detect glare in order to predict occupant satisfaction with a space. However, in order to design buildings with occupant visual comfort in mind, architects need the ability to predict glare through renderings, rather than photographs. In this paper, we compare glare measurements from high dynamic range photographs and computer renderings of the same daylit space under 240 clear and 38 cloudy sky conditions in order to assess how accurately architects can predict glare though simulation. We use two ray tracing engines, R adiance and Accelerad, on scenes illuminated by a mathematically defined sky model or by recorded outdoor luminance levels from photographs. Depending on the scene, these methods predict daylight glare probability levels due to bright sources with between 93% and 99% accuracy and discomfort glare due to contrast with between 71% and 99% accuracy. Glare predictions are more sensitive to modeling and measurement accuracy than to simulation parameters, so accurate results can come from relatively fast simulations. Furthermore, with graphics acceleration, we are able to produce accurate glare predictions within minutes using Accelerad, achieving a speedup over R adiance of between 16 and 44 times. [ABSTRACT FROM AUTHOR]

City governments and energy utilities are increasingly focusing on the development of energy efficiency strategies for buildings as a key component in emission reduction plans and energy supply strategies. To support these diverse needs, a new generation of Urban Building Energy Models (UBEM) is currently being developed and validated to estimate citywide hourly energy demands at the building level. However, in order for cities to rely on UBEMs, effective model generation and maintenance workflows are needed based on existing urban data structures. Within this context, the authors collaborated with the Boston Redevelopment Authority to develop a citywide UBEM based on official GIS datasets and a custom building archetype library. Energy models for 83,541 buildings were generated and assigned one of 52 use/age archetypes, within the CAD modelling environment Rhinoceros3D. The buildings were then simulated using the US DOE EnergyPlus simulation program, and results for buildings of the same archetype were crosschecked against data from the US national energy consumption surveys. A district-level intervention combining photovoltaics with demand side management is presented to demonstrate the ability of UBEM to provide actionable information. Lack of widely available archetype templates and metered energy data, were identified as key barriers within existing workflows that may impede cities from effectively applying UBEM to guide energy policy. [ABSTRACT FROM AUTHOR]

Over the past decades, detailed individual building energy models (BEM) on the one side and regional and country-level building stock models on the other side have become established modes of analysis for building designers and energy policy makers, respectively. More recently, these two toolsets have begun to merge into hybrid methods that are meant to analyze the energy performance of neighborhoods, i.e. several dozens to thousands of buildings. This paper reviews emerging simulation methods and implementation workflows for such bottom-up urban building energy models (UBEM). Simulation input organization, thermal model generation and execution, as well as result validation, are discussed successively and an outlook for future developments is presented. [ABSTRACT FROM AUTHOR]

• A web-based framework to rapidly generate urban building energy models was proposed. • Key stakeholders and the workflow to translate open datasets to urban building energy models and actionable insights for carbon reduction were described. • The framework is scalable, able to construct urban building geometries, assign build simulation templates, and incorporate modern open-source machine learning libraries. • The framework was first tested in a pilot study, and subsequently validated with policymakers and researchers in eight cities around the world. Policymakers are struggling to understand what specific mixes of building retrofitting upgrades are necessary to achieve carbon emission targets. Urban building energy modeling (UBEM) is a bottom-up simulation method to develop policy measures for building stocks. However, the use of UBEM tools requires hard-to-find individuals with training in multiple domains and significant setup time exacerbated through a lack of standardized building use and construction databases. To address these challenges, this paper presents UBEM.io, a novel web-based framework to rapidly generate UBEMs in an automated and scalable fashion with clear handover points. UBEM.io is designed to help any city or municipality conduct low-cost, rapid energy and carbon emissions scenario studies. UBEM.io contributes to practice and research in multiple ways: automated generation of urban-scale building geometries based on widely available inputs; assignment of building simulation templates from a pre-populated library; matching of templates to individual buildings via archetypes; and visualization of simulation results for various carbon emissions reduction pathways. The framework was piloted in Evanston, IL (USA) to build an UBEM comprising 1,363 buildings. It was then successfully deployed with representatives from eight municipalities: Braga (Portugal), Cairo (Egypt), Dublin (Ireland), Florianopolis (Brazil), Kiel (Germany), Middlebury, VT (USA), Montreal (Canada), and Singapore. [ABSTRACT FROM AUTHOR]

The Universal Thermal Climate Index (UTCI) has been linked to outdoor activity patterns and used to evaluate the effectiveness of urban interventions to improve thermal comfort. This study investigates how simulating the urban environment at increasing levels of physical accuracy impacts UTCI values along three cycling routes in Cambridge, Massachusetts. Baseline UTCI values are estimated using a local weather file, and the following increments in physical accuracy are considered: wind-scaling, shading from buildings, shading and cooling from trees, computational fluid dynamics simulations for wind speeds, and simulated surface temperatures. With bike ridership data from Bluebikes, Boston's bike-sharing program, the relationship between bike ridership patterns and UTCI values along each route is studied. Supervised machine learning models are applied to predict bike ridership based on UTCI and other predictors. UTCI simulation results show that incorporating the various increments of accuracy influences hourly UTCI values at urban areas and exposed areas differently. Incorporating local wind speeds is especially impactful for urban areas. The statistical models trained to predict hourly bike trip counts based on UTCI and other demand and weather predictors achieved a root-mean-squared error of 1.06 trips. 47% of predictions were correct, and an additional 42% of predictions were off by 1 trip. This study demonstrates the importance of spatial refinement in simulating UTCI, and motivates future research into efficient simulation methods or rules-of-thumb for deriving spatial-temporal UTCI values. Future work into building a robust predictive model would motivate the design of thermally comfortable environments for human-powered transportation in cities. [Display omitted] [ABSTRACT FROM AUTHOR]

Urban building energy and daylight modeling are bottom-up, physics-based approaches to simulate the thermal and daylight performance of neighborhoods and cities. The field has flourished in recent years due to a wider accessibility of urban data sets which contain the required information regarding building geometry and program. However, key building-level parameters, most notably window-to-wall ratio (WWR), are generally unavailable at the urban scale and tedious to collect manually. To resolve this challenge, this paper proposes a methodology to automatically extract façade opening layouts for each building adjacent to a Google Street View route. A comparison between auto-generated and manually determined WWRs for 1057 buildings in Manhattan yielded identical results (less than 10% difference) for 66% of all investigated façades. Manual and automated methods were within a 20% error in 90% of all cases. The validated method is applied to daylighting and building energy models of 2014 buildings in downtown Chicago to quantify the impact of building-by-building WWRs versus a uniform, industry-standard WWR of 40% for all buildings. The results reveal that while the total energy use predictions are within 0.2% difference, the total daylit area increases by 9.5% when the WWRs are detected. Furthermore, when individual buildings are ranked in terms of their daylight autonomy or suitability for employing different retrofitting strategies, they are oftentimes misplaced when 40% WWR assumption is used. For example, in the downtown Chicago model, 46 buildings were misclassified as belonging to the top 100 buildings with the greatest percentage-wise savings potential resulting from glazing retrofitting. • A window detection workflow valid for energy and daylight modeling is presented. • Relevance of using detected window-to-wall ratios for urban modeling is assessed. • Using window detection for developing renovation strategies is proven important. • Assigning representative window-to-wall ratios to building types is demonstrated. • Window detection is crucial in applications using rankings of individual buildings. [ABSTRACT FROM AUTHOR]

Highlights: [•] Created a phovotolvtaic-electric potential map of over 17,000 buildings. [•] Used a new method which accounts for hourly irradiation data and rooftop temperature. [•] Validated new method’s calculations against measured data from two operating photovoltaic systems. [•] Predictive error using new method was found to be less than annual climactic variation. [•] Compared new method and existing methods of predicting photovoltaic urban potential. [Copyright &y& Elsevier]

Abstract: An energy model generated during the design phase of a building could - in principal - be converted into a calibrated energy model and used to improve the building’s operational performance. However, this rarely happens in practice. Through a survey of 306 building professionals, this research investigates whether this model reuse is technically feasible, based on today’s design-phase models, and what non-technical barriers might stand in the way of its implementation. An important finding is that 75% of the engineers/energy modelers surveyed believed that their models could be used by a third party in building commissioning and operation. At the same time, many modelers voiced various reservations that might prevent them from sharing their energy models with the owner or design team. These reservations varied from a desire to protect their intellectual property, to liability concerns, to the fear of incurring additional unpaid work. In response to these findings, this paper provides suggestions for overcoming these non-technical challenges and includes references for contract precedents. [Copyright &y& Elsevier]

Abstract: This paper proposes a method for testing current and emerging daylight availability metrics such as daylighting factor, daylight autonomy, useful daylight illuminance and LEED 3.0 requirements against building occupant assessments of a daylit space. During spring 2011 the method was tested as a classroom exercise by 60 architectural students enrolled in two graduate-level building science courses in the 2nd floor studio space of le Corbusier’s Carpenter Center in Cambridge, MA, USA. The results from this test yielded that the Lighting Measurement protocol for Spatial Daylight Autonomy, that is current being developed by the Illuminating Engineering Society of North America (IESNA) daylighting metrics committee, reproduced the student assessments of the daylit area in the space more reliably than the other tested daylight availability metrics. These findings are preliminary and still need to be validated and refined in other spaces. Apart from providing valuable data points for scientific experiments, the method also has substantial educational value as a teaching exercise for architectural students to develop an intuitive understanding of contemporary daylight performance metrics, as well as a feeling of how their personal lighting preferences compare to these metrics. [Copyright &y& Elsevier]

Abstract: This paper presents a vision of how state-of-the-art computer-based analysis techniques can be effectively used during the design of daylit spaces. Following a review of recent advances in dynamic daylight computation capabilities, climate-based daylighting metrics, occupant behavior and glare analysis, a fully integrated design analysis method is introduced that simultaneously considers annual daylight availability, visual comfort and energy use: Annual daylight glare probability profiles are combined with an occupant behavior model in order to determine annual shading profiles and visual comfort conditions throughout a space. The shading profiles are then used to calculate daylight autonomy plots, energy loads, operational energy costs and green house gas emissions. The paper then shows how simulation results for a sidelit space can be visually presented to simulation non-experts using the concept of a daylighting dashboard. The paper ends with a discussion of how the daylighting dashboard could be practically implemented using technologies that are available today. [ABSTRACT FROM AUTHOR]

A simulation algorithm is proposed that predicts the lighting energy performance of manually and automatically controlled electric lighting and blind systems in private and two-person offices. Algorithm inputs are annual profiles of user occupancy and work plane illuminances. These two inputs are combined with probabilistic switching patterns, which have been derived from field data, in order to predict the status of the electric lighting and blinds throughout the year. The model features four different user types to mimic variation in control behavior between different occupants.An example application in a private office with a southern facade yields that––depending on the user type––the electric lighting energy demand for a manually controlled electric lighting and blind system ranges from 10 to 39 kW h/m2 yr. The predicted mean energy savings of a switch-off occupancy sensor in the example office are 20%. Depending on how reliably occupants switch off a dimmed lighting system, mean electric lighting energy savings due to a daylight-linked photocell control range from 60% to zero. [Copyright &y& Elsevier]

• Exoskeletons are proposed as combined structural and solar shading elements. • We combine structural sizing with solar gains and operational energy simulations. • Reduced operational emissions of 24–48% through exterior shading are achieved. • Embodied carbon savings of up to 36% (steel) and 80% (timber) in the lateral system. We propose the use of solar exoskeletons, an integrated building system that combines material efficiency in structural load transfer with passive solar gain control. This offers an impactful way to respond to the UN climate goals, as the architecture and engineering disciplines face the challenge of delivering low carbon buildings. While reducing operational and embodied emissions is often considered independently, we can show how approaching them in tandem, through a novel building system, can offer significant savings. With large spans for maximum spatial flexibility and full glazing maximizing daylight, high-rise buildings are often suboptimal in terms of their material usage from steel frame construction and cooling demand from uncontrolled solar gains. We view solar exoskeletons as a sustainable pathway for future high-rise structures – combining solar gain control through external shading with a highly efficient structural system optimized for lateral loads in tall buildings. We present an automated workflow that combines parametric modeling of architectural elements and structural simulation with Radiance-based annual radiation simulations and an operational energy model in EnergyPlus. Evaluating embodied carbon and energy use intensity of midrise and tower buildings in timber and steel, we compare hundreds of iterations for a prototypical building in Phoenix, USA. Our results show that exoskeletons can lead to embodied and operational carbon reductions in the lateral load-resisting structural system of 37–80% and 24–48%, respectively, vis-à-vis conventional construction techniques. Adding photovoltaic modules to the external shading system can lead to net zero building solutions for the buildings investigated in this case study. [ABSTRACT FROM AUTHOR]

Advances in digital fabrication and additive manufacturing have enabled the creation of geometrically complex glass structures and building components, opening up new design opportunities across scales. Quantifying and evaluating their optical performance, however, remains a technical challenge. In order to accurately predict light behavior, common approaches of daylight modeling utilizing light-backward raytracers are insufficient. This paper evaluates the use of the photon mapping approach within the Radiance render engine to simulate artificial and natural lighting conditions. 3D printed optically transparent glass components are used to benchmark the simulations. We present a workflow to gather geometric data of the glass objects and a series of validation experiments. Pairs of physical studies and digital simulations are compared to assess optical performance in the context of both indoor and outdoor lighting conditions. These experiments demonstrate that the photon mapping approach can reliably measure and predict caustic light patterns and indoor light levels with some limitations, specifically of glare and scattered light from the glass objects themselves. Image 1 • Complex glass structures cannot be simulated accurately with backward raytracing. • 3D printed glass structures are used to evaluate simulation performance. • A workflow for geometry capture and simulation is presented. • Photon mapping excels at simulating light behaviour with some limitations. • Interior light levels and caustic light patterns can be predicted reliably. [ABSTRACT FROM AUTHOR]

The presence of natural light in indoor spaces improves human health, well-being, and productivity, particularly in workplace environments. Do the social benefits of daylight translate into economic value as measured by what office tenants are willing to pay? Using a sample of 5154 office spaces in Manhattan, we pair urban daylight simulation with a hedonic valuation model to determine the marginal value of daylight in offices. Holding all other factors constant, we find that occupied spaces with access to high amounts of daylight (as measured by 55% spatial daylight autonomy) have a 5–6% value premium over occupied spaces with low amounts of daylight (as measured by less than 55% spatial daylight autonomy). We simulate the distribution of daylight on each office floor individually, taking into account architectural and location-specific characteristics. Then, using the hedonic model, we determine the added value of daylight in the office spaces. The results show, for the first time, that an estimated 74% of office spaces throughout Manhattan have low daylight, and that in a dense urban environment with differentiation in daylight levels, tenants value high daylight. Daylight value is independent of other building, neighborhood, and contract characteristics. By revealing the added value of daylight in commercial office spaces, we suggest that daylight is a key design driver and thus, should be considered in design, policy, planning, and project financing. • We find that office spaces with high levels of daylight have a 5–6% rent premium over spaces with low levels of daylight. • We find that 74% of office spaces throughout Manhattan have low levels of daylight. • We find that in a dense urban environment with differentiation in daylight levels, tenants value high daylight. [ABSTRACT FROM AUTHOR]

Highlights • Ensemble baseline development to simulate a range of future building energy scenarios. • Framework for rapid-response assessment of energy drivers for hundreds of buildings. • Procedures for campuses to track their energy policy effectiveness on an ongoing basis. • Proof-of-concept implementation for campus of Massachusetts Institute of Technology. Abstract Physics-based building energy models, once calibrated to historic energy data, are increasingly used to explore energy efficiency retrofits. When utilized for large building portfolios, such as university campuses, these calibrated models require substantial initial effort to develop, but are then typically only used once for analyzing potential building upgrades and estimating carbon reduction opportunities. This paper presents a continuous energy performance planning and tracking system for a university campus that automatically updates and compares measured against simulated building energy use for a hundred campus buildings. The system archives historic data, enables exploration of potential upgrade scenarios, and allows for the documentation of energy retrofits to individual buildings. The objectives of this tracking system are to support facility managers to ensure that their buildings are performing as intended, financial administrators to quantify potential energy savings and payback times of building upgrades and the overall university community to track campus-wide carbon emissions from buildings vis-à-vis previously defined reduction targets. A proof-of-concept implementation is presented for the authors' campus in Cambridge, Massachusetts. [ABSTRACT FROM AUTHOR]

In today's growing cities, where land is an expensive commodity and direct exposure to sunlight is a valuable asset, rooftops constitute vast underexploited areas. Particularly with growing urban environmental concerns, the potential of transforming these areas into productive spaces – either for food cultivation or energy generation – has emerged as a viable option in recent years. Both food production and energy generation have benefits in the urban environment. Rooftop farming is an environmentally and economically sustainable way of exploiting urban rooftops, reducing “food miles” and providing local jobs, while roof-integrated solar photovoltaic (PV) modules provide clean energy, are increasingly cost-effective, and offer job opportunities. In both cases, a rooftop network of production could directly supply a portion of a necessary resource – either food or electricity – to the local community while concurrently reducing the burden on the environment. To provide a basis for comparing the implementation of these productive uses of rooftops in Mediterranean cities, this article applies a Cost-Benefit Analysis (CBA) to a mixed-use neighborhood located in Lisbon to assess the following uses: (1) open-air rooftop farming on intensive green roofs; (2) food production in low-tech unconditioned Rooftop Greenhouse (RG) farms; (3) Controlled-Environment Agriculture (CEA) in high-tech RG farms; and (4) solar PV energy generation. Relative costs, cost-saving benefits and added value of these four alternative productive uses of rooftops were modeled over 50 years and deducted from present value, considering two levels of analysis: (a) effects directly incurred by the operator of the systems; and (b) societal effects on the local community. To the authors' knowledge, this is the first comprehensive comparison of rooftop PV versus rooftop farming technologies. The results have shown food production to be more beneficial than energy generation, for both the owner of the system and the local community, under the modeled conditions and given the selected items of comparison. In particular, the results show that rooftop greenhouse farming can provide significant benefits over rooftop green roof and solar PV systems when assessed from a holistic perspective that accounts for impacts on both the operator and the local community. [ABSTRACT FROM AUTHOR]

Abstract: Background: Warfarin directly inhibits vitamin K 2,3-epoxide reductase (VKOR) enzymes. Since the early 1970s, warfarin inhibition of vitamin K 2,3-epoxide reductase complex subunit 1 (VKORC1), an essential enzyme for proper function of blood coagulation in higher vertebrates, has been studied using an in vitro dithiothreitol (DTT) driven enzymatic assay. However, various studies based on this assay have reported warfarin dose–response data, usually summarized as half-maximal inhibitory concentration (IC50), that vary over orders of magnitude and reflect the broad range of conditions used to obtain VKOR assay data. Methods: We standardized the implementation of the DTT-driven VKOR activity assay to measure enzymatic Michaelis constants (Km) and warfarin IC50 for human VKORC1. A data transformation is defined, based on the previously confirmed bi bi ping-pong mechanism for VKORC1, that relates assay condition-dependent IC50 to condition-independent Ki. Results: Determination of the warfarin Ki specifically depends on measuring both substrate concentrations, both Michaelis constants for the VKORC1 enzyme, and pH in the assay. Conclusion: The Ki is not equal to the IC50 value directly measured using the DTT-driven VKOR assay. General significance: In contrast to warfarin IC50 values determined in previous studies, warfarin inhibition expressed as Ki can now be compared between studies, even when the specific DTT-driven VKOR assay conditions differ. This implies that warfarin inhibition reported for wild-type and variant VKORC1 enzymes from previous reports should be reassessed and new determinations of Ki are required to accurately report and compare in vitro warfarin inhibition results. [Copyright &y& Elsevier]

As the building stock is projected to double before the end of the half-century and the power grid is transitions to low-carbon resources, planning new construction hand in hand with the grid and its capacity is essential. This paper presents a method that combines urban building energy modeling and local planning of renewable energy sources (RES) using an optimization framework. The objective of this model is to minimize the investment and operational cost of meeting the energy needs of a group of buildings. The framework considers two urban-scale RES technologies, photovoltaic (PV) panels and small-scale wind turbines, alongside energy storage system (ESS) units that complement building demand in case of RES unavailability. The urban buildings are modeled abstractly as "shoeboxes" using the Urban Modeling Interface (umi) software. We tested the proposed framework on a real case study in a neighborhood in Chicago, Illinois, USA. The results include estimated building energy consumption, optimal capacity of the installed power supply resources, hourly operations, and corresponding energy costs for 2030. We also imposed different levels of CO2 emissions cuts. The results demonstrate that solar PV has the most prominent role in supplying local renewables to the neighborhood, with wind power making only a small contribution. Moreover, as we imposed different CO2 emissions caps, we found that ESS plays an increasingly important role at lower CO2 emissions levels. We can achieve a significant reduction in CO2 emissions with a limited increase in cost (75% emissions reduction at a 15% increase in overall energy costs). Overall, the results highlight the importance of modeling the interactions between building energy use and electricity system capacity expansion planning. • Optimization framework combines urban building energy modeling and local planning of renewables. • Urban neighborhood buildings are modeled in an abstracted manner as "shoeboxes". • Considering local wind and solar resources along with energy storage and grid supply. • PV has the most prominent role in supplying local renewables to the neighborhood. • Substantial reduction in CO2 emissions (75%) can be achieved with limited increase in energy costs (15%). [ABSTRACT FROM AUTHOR]

Abstract: Human Neuromedin U receptor 1 (hNmU-R1) is a member of G protein-coupled receptor family. For structural determination of hNmU-R1, the production of hNmU-R1 in milligram amounts is a prerequisite. Here we reported two different eukaryotic expression systems, namely, Semliki Forest virus (SFV)/BHK-21 and baculovirus/Spodoptera frugiperda (Sf9) cell systems for overproduction of this receptor. In the SFV-based expression system, hNmU-R1 was produced at a level of 5 pmol receptor/mg membrane protein and the yield could be further increased to 22 pmol receptor/mg membrane protein by supplementation with 2% dimethyl sulfoxide (DMSO). Around 8 pmol receptor/mg membrane protein could be achieved in baculovirus-infected Sf9 cells. The recombinant hNmU-R1 from SFV- and baculovirus-based systems was functional, with a K d value of [125I] NmU-23 (rat) similar to that from transiently transfected COS-7 cells, where hNmU-R1 was first identified. With the aid of 1% n-dodecyl-β-d-maltoside (LM)/0.25% cholesteryl hemisuccinate (CHS), the yield of functional hNmU-R1 could reach 80%. The recombinant receptor from Sf9 cells was purified to homogeneity. The specific binding of the purified receptor to [125I] NmU-23 (rat) indicated that the receptor is bioactive. This is the first report of successful solubilization and purification of hNmU-R1, and will enable functional and structural studies of the hNmU-R1. [Copyright &y& Elsevier]

Abstract: Neuromedin U (a neuropeptide) plays regulatory roles in feeding, anxiety, smooth muscle contraction, blood flow and pain. The physiological actions of NmU are mediated via two recently identified G protein-coupled receptors namely the neuromedin U type 1 receptor (NmU1R) and the neuromedin U type 2 receptor (NmU2R). Despite their crucial roles in cell physiology, structural information on these receptors is limited, mainly due to their low expression levels in native tissues. Here, we report the overexpression of the human NmU2R in the methylotrophic yeast Pichia pastoris and baby hamster kidney (BHK) cells using the Semliki Forest virus (SFV) system. The recombinant receptor was expressed as a fusion protein with three different affinity tags namely, the Flag tag, the histidine 10 tag and the biotinylation domain of Propionobacterium shermanii. Expression level of the recombinant receptor was 6–9pmol/mg under optimized conditions, which is significantly higher than the expression level in the native tissues. The recombinant receptor binds to its endogenous ligand neuromedin U with high affinity (Kd=0.8–1.0nM) and the binding constant for the recombinant receptor is similar to that of the wild type NmU2R. Enzymatic deglycosylation suggested that the recombinant NmU2R was glycosylated in P. pastoris, but not in BHK cells. Confocal laser scanning microscopy and immunogold labelling experiment revealed that the recombinant receptor was predominantly localized in the intracellular membranes. To our knowledge, this is the first report of heterologous overexpression of an affinity tagged recombinant NmU2R and it should facilitate further characterization of this receptor. [Copyright &y& Elsevier]

Presents a study that investigated the influence of the short-term dynamics of daylight on simulation-based predictions of the annual daylight availability in indoor illuminance using the radiance-based dynamic daylight simulation method Daysim. Role of lean buildings in office buildings construction; Importance of daylight concept planning; Significance of simulation methods in comparing daylight concepts; Factors considered in the use of the Daysim method.

• Analyzing prevailing workflow and requirements for urban building energy modeling (UBEM). • Categorizing UBEM applications into four main use cases via examples and summary case studies. • Outlining data sources, modeling approaches, and calibration methods for UBEM applications. • Introducing the minimum viable UBEM (MVU) concept. • Outlining potential and future development for UBEM applications. Urban building energy modeling (UBEM) is a bottom-up, physics-based approach to simulate the thermal performance of new or existing neighborhoods and cities. The field has flourished in recent years, creating increasingly robust urban data streams that lead from Geographical Information Systems, CityGML, Light Detection and Ranging, and tax assessor databases to synthetic hourly building energy demand profiles for current and potential future conditions. Depending on the availability of historic building energy use data, a variety of modeling, simulation, and calibration approaches as well as use cases have been proposed. This paper provides a consolidated UBEM workflow with data and process requirements, and organizes UBEM proposals into four main application categories: urban planning and new neighborhood design, stock-level carbon reduction strategies, individual building-level recommendations, and buildings-to-grid integration. For each application, the paper further introduces a minimum viable UBEM, a novel concept for UBEMs conceptualized in the spirit of a minimum viable product. Specific case studies and/or examples are listed for each application area where available. [ABSTRACT FROM AUTHOR]

Operational building energy has long been recognized as both a major contributor of as well as an opportunity to save carbon emissions. To do so, one may follow two paths, reduce the energy use in buildings (demand) or provide the required energy in more efficient ways (supply). In the literature, extensive research has been made on both fronts and at different levels of detail resulting in a myriad of tools unique to specific stages in the development timeframe of a district energy project. This manuscript describes a modeling workflow based on a new Rhinoceros-based plugin that combines an Urban Building Energy Model with a network topology optimization and a heat generation scenario model bridging the gap between the planning phase and the design phase. The new plugin builds on the foundation of umi, a Rhino-based link to Radiance and EnergyPlus developed at the MIT Sustainable Design Lab, as an addition to the demand simulation module. A demonstration project shows how the workflow serves as a stepping stone from the design environment of Rhinoceros to the technical environment of TRNSYS, thus enabling a more streamlined process to use different modeling strategies for different purposes, within the same design workflow. [ABSTRACT FROM AUTHOR]

Life cycle impacts in buildings includes operational carbon for heating, cooling, hot water, ventilation, lighting, on the one hand, and embodied carbon for material supply, production, transport, construction and disassembly, on the other. Improved operational carbon has increased the percentage of embodied carbon in the total life cycle of buildings. Kuwait is looking at enhancing the sustainability of its built environment, as there is an urgent need to expand and build new cities. This research analyses the sustainability of the Middle Eastern built environment in order to provide the most appropriate strategies to respond to this demand. Therefore, this paper looks at different alternatives to the current construction methodologies, such as cement replacement in concrete or rammed earth structural systems. The impact of three envelope and energy upgrades on the whole life cycle environmental performance of a Middle Eastern residential neighborhood is evaluated. Simulations are performed through urban modeling, resulting in a distribution of the embodied and operational impacts of buildings for the different design options. Based on the results showing embodied carbon can be lowered by 200 kg CO2e /m 2 , this paper offers guidelines for building codes and governments. [ABSTRACT FROM AUTHOR]