Headline results
12 weeks from discovery to integrated delivery
Near-100% daily RDR coverage despite missing reads
Explainable estimates with auditable data quality checks
Built to scale across ~ 60,000 meters and multiple sectors
Context
SUEZ needed reliable daily RDR (network yield) to steer operations and support contractual commitments on a large drinking-water network. The challenge: daily consumption data were incomplete and irregular—mixing non-smart meters (manual reads) and smart meters with gaps in daily values—making direct, accurate RDR computation infeasible.
The solution: Explainable imputation & RDR computation
We delivered a focused, end-to-end enhancement that turns raw, imperfect inputs into trusted daily RDRs:
Data cleaning & validation: Robust ingestion, profiling, and rule-based checks to standardise time series and surface anomalies upfront.
ML imputation for missing consumption: An ensemble-based model estimates missing daily values with feature-rich inputs (time patterns, sector data, exogenous signals). We provide SHAP-based explanations for transparency and trust.
Daily RDR calculation & distribution: Deterministic RDR computation at global and sector level, packaged for SUEZ’s cloud environment with exports/APIs for downstream use.
Challenges overcome
Incomplete telemetry: Handled non-smart meters and sporadic smart-meter gaps without sacrificing reliability.
Bias & explainability: Addressed non-random missingness with a modelled approach and human-readable justifications.
Operationalisation: Delivered a repeatable, monitored pipeline with clear failure modes and fast reruns.
