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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]

This paper evaluates the accuracy of 18 design-phase building energy models, built according to LEED Canada protocol, and investigates the effectiveness of model calibration steps to improve simulation predictions with respect to measured energy data. These calibration steps, applied in professional practice, included inputting actual weather data, adding unregulated loads, revising plug loads (often with submetered data), and other simple updates. In sum, the design-phase energy models underpredicted the total measured energy consumption by 36%. Following the calibration steps, this error was reduced to a net 7% underprediction. For the monthly energy use intensity (EUI), the coefficient of variation of the root mean square error improved from 45% to 24%. Revising plug loads made the largest impact in these cases. This step increased the EUI by 15% median (32% mean) in the models. This impact far exceeded that of calibrating the weather data, even in a sensitivity test using extreme weather years. [ABSTRACT FROM PUBLISHER]

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]

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]

• 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]

• 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]