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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]

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]

• 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]

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]