Data-driven building archetypes for urban building energy modelling

Pasichnyi, Oleksii; Wallin, Jörgen; Kordas, Olga

doi:10.1016/j.energy.2019.04.197

Cited by 94 publications

(40 citation statements)

References 41 publications

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“…On the other hand, in terms of spatial scalability, we can see how regression-based approaches can be used to model the performance of construction technologies, considering building fabric heat transfer (Bauwens and Roels, 2014;Erkoreka et al, 2016;Giraldo-Soto et al, 2018;Uriarte et al, 2019), or whole building energy behaviour (Masuda and Claridge, 2014;Lin and Claridge, 2015;Paulus et al, 2015). Going beyond single buildings, we can find examples of applications regarding building stock (Meng and Mourshed, 2017;Meng et al, 2020) and community and city scale systems (Qomi et al, 2016;Pasichnyi et al, 2019), considering also complex interactions with the urban environment and physicalstatistical interpretation of models (Afshari et al, 2017). A summary of the topics and sub-topics emerging from the review of regression-based approaches for operation phase analysis is reported in Table 3.…”

Section: Regression Models In Operational Phase Analysismentioning

confidence: 99%

“…Temporal Monthly (Abels et al, 2011;Hallinan et al, 2011a, Hallinan et al, 2011bLammers et al, 2011;Server et al, 2011) Daily (Masuda and Claridge, 2012b;Danov et al, 2013;Masuda and Claridge, 2014;Paulus et al, 2015;Hitchin and Knight, 2016;Paulus, 2017) Hourly (Jalori and Reddy, 2015b;Abushakra and Paulus, 2016) Spatial Building fabric heat transfer (Bauwens and Roels, 2014;Erkoreka et al, 2016;Giraldo-Soto et al, 2018;Uriarte et al, 2019) Building energy behaviour (Masuda and Claridge, 2014;Lin and Claridge, 2015;Paulus et al, 2015) Building stock energy behaviour (Meng and Mourshed, 2017;Meng et al, 2020) Community and city scale analysis energy behaviour (Qomi et al, 2016;Pasichnyi et al, 2019) regression-based approaches that could be used to create scalable (temporally and spatially) integrated data analysis workflows from design to operation in buildings. In this sense, we showed how data analysis techniques could be used to evaluate the impact of both technical and human factors, with the aim of reconstructing building stock data at multiple levels.…”

Section: Sub Topic Referencesmentioning

confidence: 99%

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Linking Design and Operation Phase Energy Performance Analysis Through Regression-Based Approaches

2020

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The reduction of energy usage and environmental impact of the built environment and construction industry is crucial for sustainability on a global scale. We are working towards an increased commitment towards resource efficiency in the built environment and to the growth of innovative businesses following circular economy principles. The conceptualization of change is a relevant part of energy and sustainability transitions research, which is aimed at enabling radical shifts compatible with societal functions. In this framework, building performance has to be considered in a whole life cycle perspective because buildings are long-term assets. In a life cycle perspective, both operational and embodied energy and carbon emissions have to be considered for appropriate comparability and decision-making. The application of sustainability assessments of products and practices in the built environment is itself a critical and debatable issue. For this reason, the way energy consumption data are measured, processed, and reported has to be progressively standardized in order to enable transparency and consistency of methods at multiple scales (from single buildings up to building stock) and levels of analysis (from individual components up to systems), ideally complementing ongoing research initiatives that use open science principles in energy research. In this paper, we analyse the topic of linking design and operation phase’s energy performance analysis through regression-based approaches in buildings, highlighting the hierarchical nature of building energy modelling data. The goal of this research is to review the current state of the art of in order to orient future efforts towards integrated data analysis workflows, from design to operation. In this sense, we show how data analysis techniques can be used to evaluate the impact of both technical and human factors. Finally, we indicate how approximated physical interpretation of regression models can help in developing data-driven models that could enhance the possibility of learning from feedback and reconstructing building stock data at multiple levels.

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Section: Regression Models In Operational Phase Analysismentioning

confidence: 99%

Section: Sub Topic Referencesmentioning

confidence: 99%

Linking Design and Operation Phase Energy Performance Analysis Through Regression-Based Approaches

2020

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“…The Stockholm situation has been previously documented [6,27] and well described in a figure that presents a timeline of the available power capacity contrasted with the expected growing electricity demand. However, the additional power capacity agreed upon with the heat utility is not included in the aforementioned studies.…”

Section: Power Capacity Limitsmentioning

confidence: 99%

Load Management Strategies to Increase Electric Vehicle Penetration—Case Study on a Local Distribution Network in Stockholm

Topel

Grundius²

2020

Energies

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As part of decarbonization efforts, countries are adapting their energy policies accordingly. Sweden has established ambitious energy goals, which include CO2 emissions reduction in the transport sector and high integration of renewables in the electricity sector. Coupling the two can be an enabling force towards fossil freedom. An increased share of electric vehicles is therefore a promising solution in this regard. However, there are challenges concerning the impact that a surge of electric vehicles would have on the electric infrastructure. Moreover, in Stockholm there is a shortage of power capacity due to limitations in the national transmission infrastructure, which further aggravates the situation. This paper develops a scenario-based simulation study to evaluate the impact of electric vehicle loads on the distribution grid of a Stockholm neighborhood. In this process, limiting factors and bottlenecks in the network were identified as being related to the peak power and transformer capacities for the years of 2025 and 2031. Two load management strategies and their potential to mitigate the power peaks generated from uncontrolled charging were investigated for the critical years.

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“…Robustness and resilience are two complementary concepts applied in various energy studies, including energy security assessment (Martišauskas et al 2018), building energy design and retrofits (Ascione et al 2017), grading building energy performance (e.g., energy use intensity, total energy, peak power) (Papadopoulos and Kontokosta 2019), and energy installation (e.g., heating source, ventilation system, status of refurbishment) (Pasichnyi et al 2019), to deal with the increasing uncertainties and meta-complexities that characterize energy systems in cities. Resilience refers to the ability of a system to "rebound" or withstand initial shock (interruptions) (Hughes 2015), while robustness is the capacity to maintain functions of a system (policy, political system, organization, or institution) in spite of uncertainty (Capano and Woo 2017).…”

Section: From Robustness To Resiliencementioning

confidence: 99%

Smart Energy Frameworks for Smart Cities: The Need for Polycentrism

Nyangon

2020

Handbook of Smart Cities

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Rapid growth in megacities has prompted deep transformations intended to change sociotechnical systems, deep social and institutional practices, and scientific inquiries to better understand energy and material flows of cities. Typically, these processes are defined by sociotechnical experimentation and purposive reshaping of the synergies between jurisdictions, sectors, and technical solutions required to optimize resource management and improve institutional diversity

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Data-driven building archetypes for urban building energy modelling

Cited by 94 publications

References 41 publications

Linking Design and Operation Phase Energy Performance Analysis Through Regression-Based Approaches

Linking Design and Operation Phase Energy Performance Analysis Through Regression-Based Approaches

Load Management Strategies to Increase Electric Vehicle Penetration—Case Study on a Local Distribution Network in Stockholm

Smart Energy Frameworks for Smart Cities: The Need for Polycentrism

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