Energy Operations &
Sustainability Intelligence
IoT-integrated energy monitoring platform with real-time grid operations dashboard, sustainability KPI tracking, and predictive maintenance analytics for a regional utility provider.
The Challenge
Grid operators lacked real-time visibility across distributed energy assets. IoT sensor data from hundreds of substations was collected but never synthesized. Sustainability reporting was manual, inconsistent, and always weeks behind. Maintenance was purely reactive — equipment failures caused costly, unplanned outages.
Our Solution
We built a unified IoT data integration layer connecting all sensor feeds, designed a real-time Power BI operations command center for grid visibility, created sustainability KPI dashboards aligned with ESG reporting requirements, and developed predictive maintenance analytics using ML-based anomaly detection on sensor time-series data.
The Impact
Grid operations teams gained second-level visibility across all assets. Predictive maintenance reduced unplanned outages by 22% in the first 6 months. Sustainability reporting that previously took weeks now generates automatically. CO₂ emissions data — 12.4 tonnes tracked monthly — is now reported to regulators in real time.
Dashboard View 01
Grid Operations
Command Center
Key Results
Impact Delivered
Dashboard View 02
Sustainability & ESG
Reporting Dashboard
Technical Stack
Technologies Deployed
In Context
What the Numbers
Actually Mean
Headline percentages travel badly between organisations. Here is what each figure measured, and what it depended on.
Unplanned outages, reduced by prioritising maintenance intervention by observed asset condition instead of by calendar schedule. This depends entirely on telemetry reaching an analytical layer continuously — the same models running against weekly extracts would not have produced it.
Operational state across assets and regions, with threshold alerting. The estate was already instrumented before the engagement; what was missing was the path from sensor to the person able to act on the reading.
Emissions and consumption metrics generated from operational data rather than reconstructed by hand each quarter. Beyond the time recovered, this makes figures consistent between reporting periods instead of dependent on who assembled them and which assumptions they used.
Architecture
How It Was
Actually Built
The estate was already generating the data needed; what was missing was a path from sensor to decision. Telemetry was landed through Azure IoT Hub into a time-series database sized for continuous ingestion, with aggregation applied at rest rather than retaining every reading at full granularity indefinitely — a distinction that determines whether this architecture stays affordable at scale.
Predictive maintenance was built on that foundation using PySpark for feature preparation and TensorFlow for the failure-risk models, with anomaly detection running against live streams. The 22% reduction in unplanned outages comes from prioritising intervention by actual condition rather than by calendar, which is only possible once telemetry reaches an analytical layer continuously.
Sustainability reporting was automated from the same operational data. Emissions and consumption metrics that had been reconstructed by hand each quarter are now generated from the source that already records them, which removes a recurring drain on technical time and makes figures consistent between reporting periods rather than dependent on who assembled them.
Delivery
How an Engagement
Like This Runs
Retrospective
What We Would
Do Differently
Every engagement teaches something. These are the decisions we would change if we started this one again.
The anomaly detection models were deployed before enough labelled failure history existed to tune them properly, so the early period produced more false positives than it should have. Threshold-based alerting first, with models introduced as the failure dataset matured, would have built trust faster.
We would also define the aggregation strategy earlier. Storage decisions made during the build proved harder to revise later than they would have been to plan.