Smart Water Demand Forecasting: Learning from the Data

8 pages•Published: September 20, 2018

Maria Xenochristou, Zoran Kapelan, Chris Hutton and Jan Hofman

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.

Keyphrases: demand forecasting, machine learning, random forests, water management

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 2351-2358.

Links:	https://easychair.org/publications/paper/qpH8
	https://doi.org/10.29007/wkp4

BibTeX entry

@inproceedings{HIC2018:Smart_Water_Demand_Forecasting,
  author    = {Maria Xenochristou and Zoran Kapelan and Chris Hutton and Jan Hofman},
  title     = {Smart Water Demand Forecasting: Learning from the Data},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {/publications/paper/qpH8},
  doi       = {10.29007/wkp4},
  pages     = {2351-2358},
  year      = {2018}}

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