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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]

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]

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]

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]

• 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]