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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]

In this paper the authors present an algorithm that abstracts an arbitrarily shaped set of building volumes into a group of simplified ‘shoebox’ building energy models. It is shown that for generic perimeter and core floorplans the algorithm provides a faster but comparably accurate simulation results of annual load profiles vis-à-vis multi-zone thermal models generated according to ASHRAE90.1 Appendix G guidelines. Envisioned applications range from rapid thermal model generation for urban building energy modelling to schematic architectural design. Following a description of the algorithm, its ability to produce load profiles for a mixed-use neighborhood of 121 fully conditioned buildings for a variety of climates is demonstrated. The comparison yields relative mean square errors in simulated annual building energy use intensity of five to 10 percent compared to ASHRAE 90.1 compliant building energy models while reducing simulation times by a factor of 296. [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]

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]

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: This study describes the development and validation of a Radiance model for a translucent panel. Using goniophotometer data combined with integrating sphere measurements, optical properties of the panel were derived and converted into a Radiance model using the trans and transdata material types. The Radiance model was validated in a full-scale test room with a facade featuring the translucent panel material. Over 120,000 desktop and ceiling illuminances under 24,000 sky conditions were measured and compared to simulation results using the Perez sky model and a Radiance-based daylight coefficient approach. Overall mean bias errors (MBE) below 9% and root mean square errors (RMSE) below 19% demonstrate that translucent materials can be modeled in Radiance with an even higher accuracy than was demonstrated in earlier validation studies for the plastic, metal, and glass material types. Further analysis of results suggests that the accuracy of around ±20% currently reached by dynamic Radiance/Perez/daylight coefficient calculations for many material types is sufficient for practical design considerations. A procedure is described showing how goniophotometer and integrating sphere measurements can be used to accurately model arbitrary translucent materials in Radiance using transdata function files. [Copyright &y& Elsevier]

Abstract: This paper presents findings from a web-based survey on the current use of daylight simulations in building design. The survey was administered from December 2nd 2003 to January 19th 2004. One hundred and eighty five individuals from 27 countries completed the survey. The majority of respondents worked in Canada (20%), the United States (20%), and Germany (12%). Most participants were recruited through building simulation mailing lists. Their self-reported professions ranged from energy consultants and engineers (38%) to architects and lighting designers (31%) as well as researchers (23%). They worked predominantly on large and small offices and schools. Ninety one percent of respondents included daylighting aspects in their building design. Those who did not consider daylighting blamed lack of information and unwillingness of clients to pay for this extra service. Among those participants who were considering daylighting 79% used computer simulations. This strong sample bias towards computer simulations reflects that many participants had been recruited through building simulation mailing lists. Participants named tools’ complexity and insufficient program documentation as weaknesses of existing programs. Self-training was the most common training method for daylight simulation tools. Tool usage was significantly higher during design development than during schematic design. Most survey participants used daylighting software for parameter studies and presented the results to their clients as a basis for design decisions. While daylight factor and interior illuminances were the most commonly calculated simulation outputs, shading type and control were the most common design aspects influenced by a daylighting analysis. The use of scale model measurements had rapidly fallen compared to a 1994 survey, whereas, trust in the reliability of daylighting tools has risen. While participants named a total of 42 different daylight simulation programs that they routinely used, over 50% of program selections were for tools that use the RADIANCE simulation engine, revealing the program''s predominance within the daylight simulation community. [Copyright &y& Elsevier]

Abstract: Behavioural models derived from on-going field studies can provide the basis for predicting personal action taken to adjust lighting levels, remedy direct glare, and save energy in response to physical conditions. Enabling these behavioural models in advanced lighting simulation programs, such as DAYSIM and the Lightswitch Wizard, allows for a more realistic estimate of lighting use under dynamic conditions. The current downside of these approaches is that the whole building energy impact of manual changes in blind settings and lighting use, including its effect on heating and cooling requirements, is not considered. A sub-hourly occupancy-based control model (SHOCC), which enables advanced behavioural models within whole building energy simulation, is presented. The considered behavioural models are the Lightswitch2002 algorithms for manual and automated light and blind control, while the investigated whole building energy simulation program is ESP-r. The enhanced functionality is demonstrated through annual energy simulations aiming at quantifying the total energy impact of manual control over lights and window blinds. Results show that building occupants that actively seek daylighting rather than systematically relying on artificial lighting can reduce overall primary energy expenditure by more than 40%, when compared to occupants who rely on constant artificial lighting. This underlines the importance of defining suitable reference cases for benchmarking the performance of automated lighting controls. Results also show that, depending on the proportion of buildings occupants that actively seek out daylighting, reduced lighting use through automated control may not always produce anticipated savings in primary energy for indoor climate control. In some cases, reduced lighting use is shown to even increase primary energy expenditure for indoor climate control, trimming down initial primary energy savings in lighting alone. This reveals the superiority of integrated design approaches over simpler daylighting guidelines or rules of thumb. [Copyright &y& Elsevier]

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]

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]

Urban Building Energy Modeling (UBEM) is an emerging method for exploring energy efficiency solutions at urban or district scales. More versatile than statistical models, physical bottom-up UBEMs allow planners to quantitatively assess retrofit strategies and energy supply options, leading to more effective policies and management of energy demand. The most common approach for formulating an UBEM involves segmenting a building stock into archetypes, characterizing each type, and validating the model by comparing its output to aggregated measured energy consumption. This paper presents a more detailed methodology for setting up UBEMs while faced with incomplete information about the buildings. The procedure calls for defining unknown or uncertain parameters in archetype descriptions as probability distributions and, if available, using measured energy data to update these distributions by Bayesian calibration. The methodology is validated on residential houses in Cambridge, Massachusetts. Distributions for uncertain parameters are initially generated using a training set of 399 homes with monthly electricity and gas consumption records and then applied to a larger test set of 2263 homes. The procedure is applied both for monthly and annual metered energy usage data. Results show that both annual and monthly Bayesian calibration lead to significantly better annual energy use intensity (EUI) fits compared to traditional deterministic archetype definitions. As expected, an UBEM calibrated with monthly metered data more truthfully mimics monthly EUI distributions than one based on annual data, revealing the benefit of calibrating UBEMs using the smallest measurement time step available. [ABSTRACT FROM AUTHOR]

Accommodating predicted population growth and urbanization within the UN Climate Goals poses a significant challenge for disciplines that engage with the built environment. High performing buildings of the future should offer spatial quality for their users while utilizing resources as efficiently as possible for both construction and operation. In this review, we survey the value proposition of automatic floorplan layout generation methods and their opportunities for design guidance, feedback, and optimization in the creation of new buildings, in addition to applications for inventory characterization to survey existing housing stock and guide building policy and code. We divide existing methods into three categories: bottom-up methods , top-down methods, and referential methods. We explore advantages and challenges for each approach and propose a hybrid method for future building layout automation that utilizes a new set of metrics to create sustainable buildings of the future. [Display omitted] • We survey automatic floorplan layout creation models in academia and industry. • They are categorized into bottom-up, top-down, and referential methods. • We show how they inform feedback, guidance, and optimization of new buildings. • Generative tools can be used to characterize existing buildings. • We propose a hybrid approach to utilize different strengths of methods. [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]

• UBEM proven tool in testing building energy strategy for EU's Green Deal objectives. • Novel georeferenced building archetype database successfully integrated in an UBEM. • Method is reproducible elsewhere in Europe where building archetypes are available. The European Unions (EU) Green Deal plans for a carbon neutral economy by 2050. Achieving this goal will require actions across all economic sectors, especially the building sector, which currently accounts for 40% of energy use. Residential energy use is a significant contributor, much of it due to an aging, poorly insulated building stock, much of which is concentrated in urban neighbourhoods. This research focusses on the application of an Urban Building Energy Model (UBEM) to support the Green Deal and the planned 'renovation wave'. An archetype approach is used to efficiently derive the building data needed to run the Urban Modelling Interface (UMI) to test the efficacy of energy retrofitting policies for neighbourhoods, using a case-study area of 9,000 residential buildings in a European city. Initially, UMI simulations of energy use intensity are evaluated against reported energy performance certificate data in the study area. The UBEM is then used to quantify the most cost-effective mix of envelope retrofit and onsite energy production to achieve a 60% reduction in greenhouse gas emissions by 2030. The methodology shown here is based on residential building archetypes that are available for many EU countries and the method outlined can be replicated in other urban settings. [ABSTRACT FROM AUTHOR]

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]

Significant research effort has gone into developing bottom-up, physics based, urban building energy modeling (UBEM) tools, which allow evaluating district-wide energy demand and supply strategies. In order to reduce the simulation inputs required in UBEM, groups of buildings are typically classified into representative “archetypes”. This approach necessarily simplifies the real diversity of occupant behaviors and construction variations, reducing in turn the variety of predicted building energy demands. To further understand the limitations of archetype-based modeling, this paper evaluates four characterization methods of increasing complexity, by comparing simulation results to measured Energy Use Intensity (EUI) distributions of 336 residential buildings in a district in Kuwait City. The first two methods follow a typical deterministic approach, organizing buildings into one and four archetypes respectively, while the third method introduces probabilistic uncertainty modeling for occupant related parameters. The fourth method introduces a new “Bayesian” calibration technique for archetype parameters, and applies it to occupant parameters in method three. In a first step, frequency calibrated distributions for occupancy, lighting power density, plug load density and cooling set points are generated through parametric modeling from a test set consisting of about half of the 336 buildings. The resulting joint distribution of parameters is then sampled using Latin Hyper Cube (LHC) for the probabilistic simulation of the remaining urban model. The comparison of simulated EUI distributions with measured data suggests that the proposed probabilistic Bayesian calibration leads to significantly closer predictions, with a maximum error in the 10th and 90th percentiles of 15% against 55%, 60% and 29% for the other three methods. [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]

• Modeling plant evapotranspiration within standalone EnergyPlus models. • Validation of a container farm building energy model yields a usable template model. • Accuracy of model depends on crop-specific variables, such as Leaf Area Index. • Results highlight importance of accurately modeling HVAC systems of plant factories. Plant factories have developed within urban contexts following efforts to expand local food production, create local jobs, and provide alternatives to conventional agriculture with lower greenhouse gas emissions. One plant factory system, container farms, consists of artificially lit, vertically stacked hydroponic farms inside retrofitted shipping containers and support crop production in otherwise unused locations regardless of climate and daylight availability. Given their high energy intensity, municipalities considering container farms require reliable models to study their overall environmental performance and feasibility. While previous studies have used co-simulators to consider plant-air interactions within building performance simulation (BPS) tools, energy validation studies are lacking for such models. This research presents the validation of an EnergyPlus model of a hydroponic container farm in Boston, Massachusetts based on nine months of measured data. Despite shortcomings in predicting of hourly conditioning energy, the resulting calibrated energy model achieves a Normalized Mean Bias Error of 3% and a Coefficient of Variation of the Root-Mean-Square Error of 11%. Results show that through representing plant-air interactions within EnergyPlus and modeling cooling coefficient of performance as a function of outdoor air temperature, stakeholders can reliably predict annual container farm energy use. [ABSTRACT FROM AUTHOR]