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Sustainable Cities and Society
Article . 2016 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Optimization of building energy performance by means of multi-scale analysis – Lessons learned from case studies

Authors: Lamberto Tronchin; Massimiliano Manfren; Lavinia Chiara Tagliabue;

Optimization of building energy performance by means of multi-scale analysis – Lessons learned from case studies

Abstract

Abstract The sustainability of the built environment largely depends on its energy and environmental performances. The overall objective, across the different phases of the building life cycle, is to improve building and system performances in terms of economics, comfort, environmental impact and durability. Several modelling methodologies have been developed in order to evaluate the energy performance of buildings. Generally, every modelling methodology responds effectively to some specific tasks, but there exists a lack of integration in particular with respect to the cross-disciplinary role of data. Given the multi-scale and multi-objective nature of the problem of optimization of the energy and environmental performances of the built environment, an appropriate synthesis and integration process in modelling methodologies has to be identified, addressing realistically the uncertainties inherently present in modelling strategies. Visualization and data analysis techniques are successfully used in a wide variety of applications, both in theoretical and applied domains, but questions remains about their robustness, efficiency and applicability to the problems introduced before. The paper aims to analyze critically these topics by means of case studies, showing a possible path to create a multi-scale methodology able to synthesize all the relevant aspects.

Countries
Italy, United Kingdom
Keywords

690, 330, 620, Multi-scale analysis; Building performance optimization; Integrated design process; Visualization techniques; Data analytics, Multi-scale analysisBuilding performance optimizationIntegrated design processVisualization techniquesData analytics

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    54
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
54
Top 10%
Top 10%
Top 10%