AI USE CASE
ML-Based Water Demand Forecasting
Predict hourly and daily water demand using ML to optimise reservoir management and reduce waste.
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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