Adaptive water demand forecasting for near real-time management of smart water distribution systems
- Submitting institution
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University of Exeter
- Unit of assessment
- 12 - Engineering
- Output identifier
- 1855
- Type
- D - Journal article
- DOI
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10.1016/j.envsoft.2014.06.016
- Title of journal
- Environmental Modelling and Software
- Article number
- -
- First page
- 265
- Volume
- 60
- Issue
- -
- ISSN
- 1364-8152
- Open access status
- Out of scope for open access requirements
- Month of publication
- July
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- 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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C - Water and Environment
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This unique methodology solves an important water industry problem of forecasting water consumption 24 hours ahead. The methodology is based on Artificial Intelligence and was developed initially during the £3.8M EPSRC Neptune (EP/E003192/1) project with further refinements made during the Knowledge Transfer Partnership with United Utilities (KTP008413). The demand forecasting methodology is now used by the Water Supplies Department in Hong Kong, as part of their pump scheduling system. It featured in 4 invited talks including a keynote at the prestigious Computing and Control for the Water Industry (CCWI) conference in Perugia, Italy (contact: Michele.Romano@uuplc.co.uk).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
