Abstract
Understanding, comparing, and accurately predicting water demand at different spatial scales is an important goal that will allow effective targeting of the appropriate operational and conservation efforts under an uncertain future. This study uses data relating to water consumption available at the household level, as well as postcode locations, household characteristics, and weather data in order to identify the relationships between spatial scale, influencing factors, and forecasting accuracy. For this purpose, a Gradient Boosting Machine (GBM) is used to predict water demand 1‐7 days into the future. Results show an exponential decay in prediction accuracy from a Mean Absolute Percentage Error (MAPE) of 3.2% to 17%, for a reduction in group size from 600 to 5 households. Adding explanatory variables to the forecasting model reduces the MAPE up to 20% for the peak days and smaller household groups (20‐56 households), whereas for larger aggregations of properties (100‐804 households), the range of improvement is much smaller (up to 1.2%). Results also show that certain types of input variables (past consumption and household characteristics) become more important for smaller aggregations of properties whereas others (weather data) become less important.
Original language | English |
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Article number | e2019WR026304 |
Journal | Water Resources Research |
Volume | 56 |
Issue number | 8 |
Early online date | 29 Jun 2020 |
DOIs | |
Publication status | Published - 31 Aug 2020 |
Funding
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 EP/L016214/1.
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Jan Hofman
- Department of Chemical Engineering - Professor
- Water Innovation and Research Centre (WIRC)
- Centre for Nanoscience and Nanotechnology
- Centre for Regenerative Design & Engineering for a Net Positive World (RENEW)
- Institute of Sustainability and Climate Change
- Centre of Excellence in Water-Based Early-Warning Systems for Health Protection (CWBE)
Person: Research & Teaching, Core staff, Affiliate staff