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

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

Xenochristou, Maria (University of Exeter; Stanford University)
Hutton, Chris (Wessex Water Services Ltd)
Hofman, Jan (University of Bath; KWR Water Research Institute)
Kapelan, Z. (TU Delft Sanitary Engineering; University of Exeter)

2021

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.

Random forest
Smart demand metering
Water demand forecasting

http://resolver.tudelft.nl/uuid:6daac6f0-30dc-4f17-9d4b-9ab06de59bda

https://doi.org/10.1061/(ASCE)WR.1943-5452.0001325

0733-9496

Journal of Water Resources Planning and Management, 147 (4)

Accepted Author Manuscript

Institutional Repository

journal article

© 2021 Maria Xenochristou, Chris Hutton, Jan Hofman, Z. Kapelan