Recommendation System for Meter Replacement
An ML-driven analytics system using Azure Databricks and PySpark to prioritise water-meter replacement across a 10M+ device network, reaching 92% prediction accuracy.
Device network
10M+
Prediction accuracy
92%
Incremental billing
€8.7M
Model accuracy: training epochs
92% val. accuracyReplacement priority queue
Illustrative interface with fictitious sample data, shown in place of confidential client screens.
The problem
With a 10M+ device network, there was no reliable way to know which meters most needed physical replacement: ageing units under-report usage, which quietly costs the utility in lost billing until someone notices.
What I built
A machine-learning model trained on consumption patterns, meter age and fault history that scores every device by replacement priority, running on the same Databricks/PySpark foundation as the meter-closing pipeline so it consumes the already-clean reconciled data directly.
The model reaches 92% prediction accuracy on held-out data, and its output feeds a prioritised replacement queue that field teams work down instead of replacing meters on a fixed schedule regardless of actual condition.
Impact
Better-targeted meter replacement generated roughly €8.7M in annual incremental billing by catching under-reporting meters that a fixed replacement schedule would have missed.