dc.contributor.author | Wills, P | |
dc.contributor.author | Memon, FA | |
dc.contributor.author | Savic, D | |
dc.date.accessioned | 2017-11-20T08:43:33Z | |
dc.date.issued | 2017-11 | |
dc.description.abstract | Challenges such as water scarcity and ever-increasing demand put an additional strain onto water
distribution networks. Better asset management through leakage mitigation and demand forecasting can offset the
current and future implications of these challenges. This paper shows how new high-resolution logging (e.g. 1 litre
ticks) is able to enhance traditional methods of investigating leakages (e.g. minimum night flows) and instantiate
novel methods for demand prediction (through micro-component analysis). Machine learning or other statistical
analytical techniques coupled with the high-resolution data can be used in an adaptive way for leakage detection and
demand forecasting. As a proof of concept, this paper investigates example datasets obtained from a UK based water
company. The analyses suggest that it is possible to: extrapolate leakage from night flow time series data; predict
water consumption patterns for different types of households and create consumption profiles based upon water user
characteristics/behaviour. | en_GB |
dc.description.sponsorship | The authors would like to thank the EPSRC funding from WISE Centre for Doctoral Training. The authors also acknowledge the provision of anonymised data and financial support from South West Water, UK. | en_GB |
dc.identifier.citation | International Conference on Sustainable Development in Civil Engineering, 2017-11-23, 2017-11-25, MUET, Jamshoro | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/30367 | |
dc.language.iso | en | en_GB |
dc.publisher | Mehran University Research Journal of Engineering & Technology | en_GB |
dc.relation.url | http://icsdc.muet.edu.pk/ | en_GB |
dc.relation.url | http://hdl.handle.net/10871/30745 | |
dc.subject | leakage | en_GB |
dc.subject | water distribution | en_GB |
dc.subject | micro-component analysis | en_GB |
dc.subject | demand forecasting | en_GB |
dc.title | High-resolution domestic water consumption data – Scope for leakage management and demand prediction | en_GB |
dc.type | Conference paper | en_GB |
dc.date.available | 2017-11-20T08:43:33Z | |
dc.description | This is the author accepted manuscript. | en_GB |
dc.description | There is another ORE record for this publication: http://hdl.handle.net/10871/30745 | |