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 1438
Special Issue Editors
Interests: data-driven methods; machine learning; uncertainty quantification; domestic retrofit
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
