Smart Water Demand Forecasting: Learning from the Data

Maria Xenochristou, Zoran Kapelan, Chris Hutton, Johannes Hofman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Accurate forecasts of demand are essential for water utilities in order to manage, plan, and optimize the operation of their network. This work aims to develop a new method for short- term water demand forecasting by utilizing a new data-driven approach based on Random Forests, as well as consumption recordings, household, and socio-economic characteristics, and weather data. Initial results, obtained on real-life consumption data from the UK, demonstrate the potential of this method and show the importance of disaggregating consumption when attempting to determine the influence of weather on water demand. In this study, adding weather input to the model achieved improved forecasting accuracy, especially for the aggregation of properties with medium occupancy and affluent residents during summer months.
Original languageEnglish
Title of host publicationHIC 2018. 13th International Conference on Hydroinformatics
PublisherEasyChair Publications
Pages2351-2358
Number of pages8
Volume3
DOIs
Publication statusPublished - 20 Sep 2018
EventInternational Conference on Hydroinformatics - Palermo, Italy
Duration: 1 Jul 20186 Jul 2018
Conference number: 13
https://www.hic2018.org/

Publication series

Name
PublisherEasyChair Publications
ISSN (Electronic)2516-2330

Conference

ConferenceInternational Conference on Hydroinformatics
Abbreviated titleHIC 2018
CountryItaly
CityPalermo
Period1/07/186/07/18
Internet address

Keywords

  • demand forecasting
  • machine learning
  • random forests
  • water management

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