Nemesyst: A Hybrid Parallelism Deep Learning-Based Framework Applied for Internet of Things Enabled Food Retailing Refrigeration Systems
- Submitting institution
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University of Lincoln
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 37181
- Type
- D - Journal article
- DOI
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10.1016/j.compind.2019.103133
- Title of journal
- Computers in Industry
- Article number
- -
- First page
- 103133
- Volume
- 113
- Issue
- -
- ISSN
- 0166-3615
- Open access status
- Access exception
- Month of publication
- October
- Year of publication
- 2019
- URL
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https://doi.org/10.1016/j.compind.2019.103133
- 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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3
- Research group(s)
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-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The developed approach, predicting which refrigerators to select and how long to turn them off, whilst maintaining food quality and safety, was the first study to show how to optimize refrigerator systems with ML at scale, based on real data provided by Tesco. It enabled Tesco to optimize demand side response for reacting to the requirements of National Grid and IMS Evolve to improve their respective control systems. This resulted in public announcements https://www.current-news.co.uk/news/tesco-trials-offering-up-fridges-for-frequency-response;
https://www.foodserviceequipmentjournal.com/imitation-supermarket-helps-tesco-test-boundaries-of-its-refrigeration-systems/;
https://www.theguardian.com/business/2019/jun/23/cool-running-supermarket-fridges-could-help-power-uk;
https://ktn-uk.org/casestudy/tesco/, for Tesco Plc, and in
https://www.ims-evolve.com/news/supermarkets-cold-storage-could-provide-national-battery-for-uk-grid.html, for IMS Evolve.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -