Computational Methods in Building Energy Efficiency Research

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".

Deadline for manuscript submissions: 20 October 2024 | Viewed by 1892

Special Issue Editors


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Guest Editor
School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
Interests: data-driven methods; machine learning; uncertainty quantification; domestic retrofit

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Co-Guest Editor
Civil and Structural Engineering, University of Sheffield, Sheffield S10 2TN, UK
Interests: data-driven urban analytics; urban scaling and allometry; network analysis; stocks and metabolism

Special Issue Information

Dear Colleagues,

A major challenge to achieving global decarbonisation targets is building retrofitting and increasing energy efficiency. Meeting net-zero targets will require increasingly impractical rates of deployment across a range of energy-efficient retrofit measures, e.g., heat pumps, insulation, glazing, etc. Computational methods that are utilized in research in the built environment is a rapidly emerging field. Large-scale, data-driven solutions are becoming essential for analysis and optimisation of retrofit interventions.

Furthermore, while a body of work is emerging to address retrofitting and other challenges in meeting net-zero decarbonisation targets in the built environment, considerations of future sustainability are largely absent. With large shifts in climate and weather patterns predicted to occur over the next few decades, considering the effects of future climate on the choice and sustainability of energy-efficient retrofitting is becoming increasingly important. Predictive modelling incorporating future climate scenarios is of particular interest for this Special Issue.

The aims of this Special Issue are to explore recent developments in computational methods for establishing energy efficiency in buildings, including around targeted retrofit interventions; predictive modelling, including of future climate change; statistical archetypes; and optimisation. Topics related to computational methods and energy efficiency in buildings may include, but are not limited to:

  • Artificial intelligence and machine learning;
  • Digital twins;
  • Predictive analytics;
  • Extreme climate change and future climate scenarios;
  • Decision support systems;
  • Optimisation;
  • Uncertainty quantification.

Dr. Wil Ward
Dr. Hadi Arbabi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational methods
  • artificial intelligence
  • retrofit
  • machine learning
  • digital twins
  • energy efficiency
  • future climate change
  • predictive analysis
  • optimisation
  • decision support

Published Papers (2 papers)

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23 pages, 3087 KiB  
Article
A Heat Loss Sensitivity Index to Inform Housing Retrofit Policy in the UK
by Christopher Tsang, James Parker and David Glew
Buildings 2024, 14(3), 834; https://doi.org/10.3390/buildings14030834 - 20 Mar 2024
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Abstract
A substantial number of dwellings in the UK have poor building fabric, leading to higher carbon emissions, fuel expenses, and the risk of cold homes. To tackle these challenges, domestic energy efficiency policies are being implemented. One effective approach is the use of [...] Read more.
A substantial number of dwellings in the UK have poor building fabric, leading to higher carbon emissions, fuel expenses, and the risk of cold homes. To tackle these challenges, domestic energy efficiency policies are being implemented. One effective approach is the use of energy models, which enable sensitivity analysis to provide valuable insights for policymakers. This study employed dynamic thermal simulation models for 32 housing archetypes representative of solid-walled homes in the UK to calculate the heat loss and the sensitivity coefficient per building fabric feature, after which a metric Heat Loss Sensitivity (HLS) index was established to guide the selection of retrofit features for each archetype. The building fabric features’ inputs were then adjusted to establish both lower and upper bounds, simulating low and high performance levels, to predict the how space heating energy demand varies. The analysis was extended by replicating the process with various scenarios considering climates, window-to-wall ratios, and overshadowing. The findings highlight the external wall as the primary consideration in retrofitting due to its high HLS index, even at high window-to-wall ratios. It was also established that dwelling type is important in retrofit decision-making, with floor and loft retrofits having a high HLS index in bungalows. Furthermore, the analysis underlines the necessity for Standard Assessment Procedure assessors to evaluate loft U-value and air permeability rates prior to implementing retrofit measures, given the significance of these factors in the lower and upper bounds analysis. Researchers globally can replicate the HLS index approach, facilitating the implementation of housing retrofit policies worldwide. Full article
(This article belongs to the Special Issue Computational Methods in Building Energy Efficiency Research)
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Review

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21 pages, 2767 KiB  
Review
A Systematic Review of Applications of Generative Design Methods for Energy Efficiency in Buildings
by Phattranis Suphavarophas, Rungroj Wongmahasiri, Nuchnapang Keonil and Suphat Bunyarittikit
Buildings 2024, 14(5), 1311; https://doi.org/10.3390/buildings14051311 - 7 May 2024
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Abstract
Energy efficiency is a principle of architectural design that reduces environmental impact. Generative design can offer alternative options to improve energy efficiency in buildings, but significant gaps exist in the application due to accessing complex knowledge. This study aimed to explore publications on [...] Read more.
Energy efficiency is a principle of architectural design that reduces environmental impact. Generative design can offer alternative options to improve energy efficiency in buildings, but significant gaps exist in the application due to accessing complex knowledge. This study aimed to explore publications on generative design and energy efficiency in buildings and identify generative methods for energy efficiency topics. This study conducted a systematic review using the PRISMA methodology in December 2023 by searching publications from databases including Scopus, Google Scholar, and Thai Journals Online. Descriptive analysis examined 34 articles, showing the publication year, source, and citations. Comparative qualitative and descriptive analysis identified generative methods. Publications are increasing over time, and further growth is expected related to the accessibility of computational design and practical applications. Tools and frameworks demonstrated reduced energy usage compared to prototypes or traditional design approaches. The most studied is thermal performance, which was reduced by 28%. Energy performance achieved up to a 23.30% reduction, followed by others and daylighting. In addition to single-topic studies, there are also studies with multiple topics. Evolutionary algorithms are standard. Parametric search strategies have increased. Exploration reveals rule-based and mixed methods. Machine learning and AI garner attention. Full article
(This article belongs to the Special Issue Computational Methods in Building Energy Efficiency Research)
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