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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University of Exeter
- Unit of assessment
- 12 - Engineering
- Output identifier
- 2014
- 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
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- 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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F - Engineering Management
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This study presents a novel optimized rule development process for energy savings in the built environment using Artificial Neural Networks and Genetic Algorithms, which is one of the first data driven approach. The research was developed in the scope of EC-FP7 funded project, KnoholEM. It has been applied in a combined public-residential building (Forum Building, in Eindhoven), the contact for this building is Peter Brills (p.brils@atriensis.nl). Between November-December 2014 a 25.71% heating energy saving was achieved. The projects' outcomes, the developed artificial intelligence tools, publications and networking, supported the team in submitting grant applications against three H2020 project calls.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -