Developing a framework for urban building life cycle energy map with a focus on rapid visual inspection and image processing

Mohammadiziazi, Rezvan; Bilec, Melissa M.

doi:10.1016/j.procir.2019.01.048

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…Similarly, Guo et al [43] converted heights to floor counts based on a 3.5 m floor-to-floor height assumption. Another study determined floor-to-floor height according to building function [114]. Other articles used simple assumptions regarding floor counts to estimate total floor area and quantify materials [32,37].…”

Section: Bottom-up Approachesmentioning

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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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: Bottom-up Approachesmentioning

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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“…-can be proposed for residential household occupancy prediction (RHOP) over a geographical area that utilizes the HOEC dataset and will allow an accurate estimation of the residential electricity demand. Second, a deep learning artificial intelligence tool can be applied for transformer-household connectivity identification (THCI); this is achieved through a comprehensive analyses of the Variational Auto Encoder and K-means Clustering methods using both the measured node voltages at the location of households and building footprint GIS information to characterize the sub-network cluster of households to their parent transformers [105,106]. Third, when the first two modules are coupled, the real-time RHOP and THCI can initiate proactive decision making for online reconfiguration of the network so as to ensure an effective transformer-feeder load management, continuous supplydemand balance, availability of grid-support volt-var control functions, and required resource allocation.…”

Section: Visions For a Pandemic-prepared Futurementioning

confidence: 99%

Electric Power Grids Under High-Absenteeism Pandemics: History, Context, Response, and Opportunities

et al. 2020

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Widespread outbreaks of infectious disease, i.e., the so-called pandemics that may travel quickly and silently beyond boundaries, can significantly upsurge the morbidity and mortality over largescale geographical areas. They commonly result in enormous economic losses, political disruptions, social unrest, and quickly evolve to a national security concern. Societies have been shaped by pandemics and outbreaks for as long as we have had societies. While differing in nature and in realizations, they all place the normal life of modern societies on hold. Common interruptions include job loss, infrastructure failure, and political ramifications. The electric power systems, upon which our modern society relies, is driving a myriad of interdependent services, such as water systems, communication networks, transportation systems, health services, etc. With the sudden shifts in electric power generation and demand portfolios and the need to sustain quality electricity supply to end customers (particularly mission-critical services) during pandemics, safeguarding the nation's electric power grid in the face of such rapidly evolving outbreaks is among the top priorities. This paper explores the various mechanisms through which the electric power grids around the globe are influenced by pandemics in general and COVID-19 in particular, shares the lessons learned and best practices taken in different sectors of the electric industry in responding to the dramatic shifts enforced by such threats, and provides visions for a pandemic-resilient electric grid of the future.

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“…Therefore, analyzing buildings at large scale and how they consume energy during operation in accordance to different factors such as weather condition, geographic region, building activity (use type), etc. will improve our understanding and aid policy makers and city planners in making informative decisions regarding regional energy and climate change mitigation policies as well as resiliency planning [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning for Predicting Building Energy Use at Different Temporal and Spatial Resolution under Climate Change in USA

Mohammadiziazi

Bilec

2020

Buildings

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Given the urgency of climate change, development of fast and reliable methods is essential to understand urban building energy use in the sector that accounts for 40% of total energy use in USA. Although machine learning (ML) methods may offer promise and are less difficult to develop, discrepancy in methods, results, and recommendations have emerged that requires attention. Existing research also shows inconsistencies related to integrating climate change models into energy modeling. To address these challenges, four models: random forest (RF), extreme gradient boosting (XGBoost), single regression tree, and multiple linear regression (MLR), were developed using the Commercial Building Energy Consumption Survey dataset to predict energy use intensity (EUI) under projected heating and cooling degree days by the Intergovernmental Panel on Climate Change (IPCC) across the USA during the 21st century. The RF model provided better performance and reduced the mean absolute error by 4%, 11%, and 12% compared to XGBoost, single regression tree, and MLR, respectively. Moreover, using the RF model for climate change analysis showed that office buildings’ EUI will increase between 8.9% to 63.1% compared to 2012 baseline for different geographic regions between 2030 and 2080. One region is projected to experience an EUI reduction of almost 1.5%. Finally, good data enhance the predicting ability of ML therefore, comprehensive regional building datasets are crucial to assess counteraction of building energy use in the face of climate change at finer spatial scale.

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Developing a framework for urban building life cycle energy map with a focus on rapid visual inspection and image processing

Cited by 8 publications

References 13 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

Electric Power Grids Under High-Absenteeism Pandemics: History, Context, Response, and Opportunities

Application of Machine Learning for Predicting Building Energy Use at Different Temporal and Spatial Resolution under Climate Change in USA

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