This study utilizes a rich UK data set of smart demand metering data, household characteristics, and weather data to develop a demand forecasting methodology that combines the high accuracy of machine learning models with the interpretability of statistical methods. For this reason, a random forest model is used to predict daily demands 1 day ahead for groups of properties (mean of 3.8 households/group) with homogenous characteristics. A variety of interpretable machine learning techniques [variable permutation, accumulated local effects (ALE) plots, and individual conditional expectation (ICE) curves] are used to quantify the influence of these predictors (temporal, weather, and household characteristics) on water consumption. Results show that when past consumption data are available, they are the most important explanatory factor. However, when they are not, a combination of household and temporal characteristics can be used to produce a credible model with similar forecasting accuracy. Weather input has overall a mild to no effect on the model's output, although this effect can become significant under certain conditions.

Original languageEnglish
Article number1325
JournalJournal of Water Resources Planning and Management
Issue number4
Early online date20 Jan 2021
Publication statusPublished - 1 Apr 2021

Bibliographical note

Funding Information:
This study was funded as part of the Water Informatics Science and Engineering Centre for Doctoral Training (WISE CDT) under a grant from the Engineering and Physical Sciences Research Council (EPSRC) (Grant No. EP/L016214/1). The data for this study were made available by Wessex Water.

Publisher Copyright:
© 2021 American Society of Civil Engineers.

Copyright 2021 Elsevier B.V., All rights reserved.


  • Random forest
  • Smart demand metering
  • Water demand forecasting

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Geography, Planning and Development
  • Water Science and Technology
  • Management, Monitoring, Policy and Law


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