An ANN-GA semantic rule-based system to reduce the gap between predicted and actual energy consumption in buildings
- Submitting institution
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Cardiff University / Prifysgol Caerdydd
- Unit of assessment
- 12 - Engineering
- Output identifier
- 96526963
- Type
- D - Journal article
- DOI
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10.1109/TASE.2015.2490141
- Title of journal
- IEEE Transactions on Automation Science and Engineering
- Article number
- -
- First page
- 1351
- Volume
- 14
- Issue
- 3
- ISSN
- 1545-5955
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2015
- URL
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http://dx.doi.org/10.1109/TASE.2015.2490141
- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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1
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The research has evidenced the role of BIM in reducing the energy performance gap by up to 50%. Its originality and significance lie in its semantic-based approach to managing energy performance in buildings, using machine learning techniques. It is informing the delivery of BIM Level 3 agenda through Rezgui’s BRE Chair and his expert role in the Digital Built Britain initiative. The research has led into a patent (US patent No_16/513,471) and a portfolio of 2 FP7 (PERFORMER_Grant_609154 and RESILIENT_grant_314671) and 2 H2020 (THERMOSS_Grant_723562 and PENTAGON_Grant_731125) projects, involving collaboration with 20 major organisations.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -