Energy Analytics
& Grid Intelligence
Real-time operations, asset and sustainability analytics for energy and utilities — built on direct IoT and sensor integration rather than periodic manual readings.
The Challenge
Sensor data is collected but never reaches the people making operational decisions. Maintenance runs on a calendar rather than on condition. Sustainability reporting is assembled manually each quarter, and outages are explained after the fact instead of predicted.
Our Approach
We integrate IoT and SCADA sources into a streaming data layer, build condition-based maintenance analytics on top, and automate the sustainability reporting that currently consumes a quarter of somebody's year.
What You Get
Grid and asset condition visible continuously. Maintenance prioritised by actual risk. Sustainability metrics produced automatically from operational data rather than reconstructed by hand.
What We Build
Built for
Energy & Utilities
Live operational state across assets and regions, with threshold alerting.
Sensor and telemetry streams landed into a governed analytical layer.
Condition-based failure risk scoring so intervention precedes the outage.
Emissions and consumption metrics generated automatically from operational data.
Root cause, duration and recurrence tracked across the asset base.
Demand patterns by region and segment, supporting capacity planning.
Proven Results
Measured Outcomes
From Real Engagements
Proof
See It
In Practice
Technical Stack
Technologies
We Use
Context
Why Energy & Utilities Analytics
Is Different
Energy and utilities operations are among the most heavily instrumented environments in industry, and among the least well served by analytics. Sensors generate continuous telemetry, SCADA systems record it, and the people making operational decisions frequently work from a daily summary. The data exists; the path from sensor to decision does not.
Maintenance is where the cost shows up most clearly. Calendar-based schedules service assets that do not need it and miss the ones that do. Condition-based prioritisation requires the telemetry to reach an analytical layer continuously, which is an integration problem rather than a modelling one.
Sustainability reporting is the other recurring burden. Emissions and consumption metrics are usually reconstructed by hand each quarter from the same operational data that already exists in the estate. Automating that is straightforward once the integration layer is in place, and it removes a recurring drain on a technical team's time.
How It Runs
A Clear Process.
No Surprises.
FAQ
Energy & Utilities Questions
We Get Asked
In most cases yes, via historian databases, OPC interfaces or vendor APIs depending on what is exposed. We work with the integration path your system supports rather than requiring a change to it.
It depends on failure history and sensor coverage. Where you have both, condition-based risk scoring is achievable and is where the return concentrates. Where you have telemetry but little failure history, we start with threshold-based alerting and build the dataset that makes prediction possible later.
Yes, and it is usually one of the clearer wins. Emissions and consumption metrics generated automatically from operational data remove a recurring manual exercise and improve consistency between reporting periods.
With an architecture designed for it: streaming ingestion, appropriate aggregation at rest, and a warehouse layer sized for analytical query patterns rather than raw retention of every reading at full granularity.
How We Check the Data
Validating Meter Data
Before It Is Reported
A meter reporting every 15 minutes should send 96 readings a day. We count them and show completeness next to every total, so nobody reads a number built on missing data. Worked example, with made-up numbers, for one day:
| Meter | Received | Expected | Status | Completeness |
|---|---|---|---|---|
| M-101 | 96 | 96 | Complete | 100.0% |
| M-102 | 90 | 96 | 6 gaps | 93.8% |
| M-103 | 98 | 96 | 2 duplicates | Needs de-duplication |
Then we report energy per unit of output, because a busy week uses more power than a quiet one. If a line used 12,400 kWh to make 8,000 units, that is 1.55 kWh per unit. If next week it is 1.80, something changed even if total kWh fell. This needs production counts joined to energy readings on the same asset and period, which is why we keep one governed asset table.
Questions we hear
At the resolution your decisions need. Cost reporting works with 15-minute or hourly readings; fault detection on a machine can need every minute. Higher resolution multiplies storage and cleaning work.
Yes, once it is validated and summarised. Very high-frequency raw streams belong in a store built for them first, with Power BI reading hourly or daily aggregates.
We flag them, count them and show completeness on the dashboard. A gap is filled only under a stated rule, such as carrying a value forward for a short outage, and filled values are marked.
Usually not. We start with the data you already collect and add sensors where the first analysis shows a blind spot.