AI USE CASE
ML-Based Water Demand Forecasting
Predict hourly and daily water demand using ML to optimise reservoir management and reduce waste.
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Run the diagnostic →What it is
By combining weather data, seasonal patterns, and population metrics, machine learning models forecast water demand at hourly and daily granularity. Utilities typically achieve 15-30% reductions in reservoir overflow events and energy savings of 10-20% by aligning pump schedules with predicted demand. Accurate forecasts also help defer capital expenditure on infrastructure by extending the effective life of existing assets.
Data you need
Multi-year historical water consumption records at hourly or daily resolution, combined with weather data (temperature, precipitation) and population/demographic data for the service area.
Required systems
- erp
- data warehouse
Why it works
- Establish automated data pipelines pulling weather, SCADA, and consumption data in near real-time.
- Involve operations engineers in model validation to build trust and surface domain knowledge.
- Implement a regular model retraining cadence tied to seasonal cycles and population updates.
- Define clear KPIs (overflow events, pump energy cost) before deployment to measure impact objectively.
How this goes wrong
- Insufficient historical consumption data at the required granularity, leading to poorly trained models.
- Weather and demographic data feeds are inconsistent or not integrated, degrading forecast accuracy.
- Operational teams distrust model outputs and revert to manual planning, nullifying savings.
- Model performance degrades over time without a retraining pipeline to capture shifting demand patterns.
When NOT to do this
Do not deploy this solution if the utility lacks at least two years of hourly-resolution consumption data, as the model will lack the seasonal and weather-correlated patterns needed for reliable forecasts.
Vendors to consider
- Schneider Electric (EcoStruxure Water)www.se.com/ww/en/work/solutions/for-business/water-wastewater/ →
- Suez Smart Solutionswww.suez.com/en/our-offer/our-solutions/water-technologies-and-solutions/smart-networks →
- Dataikuwww.dataiku.com →
- Xylem Vue (Analytics)www.xylem.com/en-us/products--services/digital-solutions/xylem-vue/ →
Sources
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