Short-Term Forecasting of Household Water Demand in the UK Using an Interpretable Machine-Learning Approach

Maria Xenochristou, Chris Hutton, Jan Hofman, Zoran Kapelan

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
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Abstract

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
Number of pages14
JournalJournal of Water Resources Planning and Management
Volume147
Issue number4
DOIs
Publication statusPublished - 2021

Keywords

  • Random forest
  • Smart demand metering
  • Water demand forecasting

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