Urban building energy model: Database development, validation, and application for commercial building stock

Mohammadiziazi, Rezvan; Copeland, Samuel; Bilec, Melissa M.

doi:10.1016/j.enbuild.2021.111175

Cited by 41 publications

(24 citation statements)

References 39 publications

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“…Although remote sensing methods such as LiDAR analysis [9] and the processing of space-borne sensor data [123] have been proposed to retrieve building height, this is an extra step in the creation of bottom-up material stock models. To resolve this barrier, cities and countries can consider making threedimensional models of existing buildings at scale.…”

Section: Discussionmentioning

confidence: 99%

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Building material stock analysis is critical for effective circular economy strategies: a comprehensive review

Mohammadiziazi

Bilec

2022

Environ. Res.: Infrastruct. Sustain.

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Buildings account for the largest share of accumulated materials and waste. Tracking material composition, quantity, and location of these materials, known as building material stock analysis (MSA), is a first step enabling the reuse or repurposing of materials, key strategies of the circular economy. While the number of building MSAs is growing, there is a need to coalesce methods, data, and scope. Therefore, in this work, we reviewed and evaluated 62 journal and conference articles on MSA of buildings from different angles including scope, boundaries, archetype classification, material intensity determination, approaches (i.e., bottom-up, top-down, remote sensing), and quantity of materials to identify barriers, gaps, and opportunities of the area along with its implications on decision making, policy, and regulations. We cataloged the three major approaches of MSAs and discussed their advantages and shortcomings. We also created a comprehensive directory of building archetypes, references, and materials for future researchers. Most of the studies expectedly estimated that concrete had the largest mass compared to other materials; however, mass-based distribution of materials showed significant variations in different building stocks across the world. Also, embedded plastics and their types remained under-represented in current studies. A major barrier to MSA is related to lack of physical attributes, GIS, design, and construction data. Policy makers can play a role in mitigating data barriers through instituting regulations, which enforce reporting building-related data during permitting process. Furthermore, outcomes of building MSA can help policy makers in considering incentives for design and construction that utilize these abundant building materials.

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Section: Discussionmentioning

confidence: 99%

“…Simply put, one cannot mine without knowledge of where the material is located, along with the type, quantity, quality and value of the material [8]. Information about buildings in several countries including the United States is usually disparate, sparse and granular [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Building material stock analysis is critical for effective circular economy strategies: a comprehensive review

Mohammadiziazi

Bilec

2022

Environ. Res.: Infrastruct. Sustain.

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“…Primary source data for buildings are increasingly available for methodological improvements [52][53][54]. These data, as well as emissions from municipal buildings, are not subject to the confidentiality constraints typical of utility data.…”

Section: Discussionmentioning

confidence: 99%

Comparing sources of uncertainty in community greenhouse gas estimation techniques

Blackhurst

Matthews

2022

Environ. Res. Lett.

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Independent methods for estimating local greenhouse gas emissions have been developed utilizing different instrumentation, sampling, and estimation techniques. Comparing independent estimates theoretically improves understanding of emission sources. However, each method estimates emissions with varying fidelity, complicating comparisons across methods, cities, and over time. It is thus difficult for decision-makers to judge how to use novel estimation methods, particularly when the literature implies a singular method is best. We review 650 articles to define the scope and contours of estimation methods, develop and apply an uncertainty typology, and describe the strengths and weaknesses of different approaches. We identify two prominent process-based estimation techniques (summing of utility bills and theoretical modeling), three techniques that attribute observed atmospheric CO2 to source locations (eddy covariance footprinting, dispersion models, and regression), and methods that spatiotemporally distribute aggregate emissions using source proxies. We find that “ground truth” observations for process-based method validation are available only at the aggregate scale and emphasize that validation at the aggregate scale does not imply a valid underlying spatiotemporal distribution. “Ground truth” observations are also available post-combustion as atmospheric CO2 concentrations. While dispersion models can spatially and temporally estimate upwind source locations, missing validation data by source introduces unknowable uncertainty. We find that many comparisons in the literature are made across methods with unknowable uncertainty, making it infeasible to rank methods empirically. We see promise in the use of regression for source attribution owing to its controlling for confounding emissions, flexibly accommodating different source proxies, explicitly quantifying uncertainty, and growing availability of CO2 samples for modeling. We see developing cross-walks between land use and end-use sectors as an important step to comparing process-based methods with those attributing atmospheric CO2 to sources. We suggest pooling data streams can produce better decision support resources for cities with proper attribution of empirical fidelity.

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“…For an efficient simulation workflow, parameters such as heating and cooling schedules, and material properties, etc., are enriched directly in the input data model. Approaches made by previous studies [21,29,[34][35][36][37][38][39] relate to pre-defined archetypes for enrichment. An archetype is defined by selecting a sample building with measured data or by using statistical building-related data [40].…”

Section: Archetype-based Approachmentioning

confidence: 99%

Open-Source Tool for Transforming CityGML Levels of Detail

et al. 2021

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Urban Building Energy Modelling (UBEM) requires adequate geometrical information to represent buildings in a 3D digital form. However, open data models usually lack essential information, such as building geometries, due to a lower granularity in available data. For heating demand simulations, this scarcity impacts the energy predictions and, thereby, questioning existing simulation workflows. In this paper, the authors present an open-source CityGML LoD Transformation (CityLDT) tool for upscaling or downscaling geometries of 3D spatial CityGML building models. With the current support of LoD0–2, this paper presents the adapted methodology and developed algorithms for transformations. Using the presented tool, the authors transform open CityGML datasets and conduct heating demand simulations in Modelica to validate the geometric processing of transformed building models.

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Urban building energy model: Database development, validation, and application for commercial building stock

Cited by 41 publications

References 39 publications

Building material stock analysis is critical for effective circular economy strategies: a comprehensive review

Building material stock analysis is critical for effective circular economy strategies: a comprehensive review

Comparing sources of uncertainty in community greenhouse gas estimation techniques

Open-Source Tool for Transforming CityGML Levels of Detail

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