TY - JOUR
T1 - Short-Term Forecasting of Household Water Demand in the UK Using an Interpretable Machine-Learning Approach
AU - Xenochristou, Maria
AU - Hutton, Chris
AU - Hofman, Jan
AU - Kapelan, Zoran
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Random forest
KW - Smart demand metering
KW - Water demand forecasting
UR - http://www.scopus.com/inward/record.url?scp=85099548059&partnerID=8YFLogxK
U2 - 10.1061/(ASCE)WR.1943-5452.0001325
DO - 10.1061/(ASCE)WR.1943-5452.0001325
M3 - Article
AN - SCOPUS:85099548059
SN - 0733-9496
VL - 147
JO - Journal of Water Resources Planning and Management
JF - Journal of Water Resources Planning and Management
IS - 4
M1 - 1325
ER -