EnergyAI & LLM Integration

35% reduction in unplanned downtime, £1.2M saved annually

IndustryEnergy / Utilities

Team7 engineers

Timeline18 weeks

The Challenge

An energy infrastructure operator experienced 340 hours of unplanned equipment downtime annually across 180 assets, costing £1.8M in emergency repairs and lost generation capacity. Their maintenance team operated on fixed-schedule preventive maintenance, resulting in both premature part replacements and surprise failures. Sensor data from 8,000 IoT monitors was collected but never analysed in real time.

The Solution

TECHTIMIZE built an AWS IoT Core ingestion pipeline collecting 8,000 sensor streams into AWS Timestream time-series database. A TensorFlow LSTM anomaly detection model trained on 2 years of sensor data predicts equipment failures 48 hours in advance with 91% precision. A Grafana operations dashboard visualises asset health scores in real time, surfacing failure predictions ranked by impact severity. Maintenance teams receive 48-hour advance warnings via PagerDuty, enabling planned interventions.

Technology Stack

Pythonscikit-learnAWS IoT CoreAWS TimestreamGrafanaNode.jsReactTensorFlow

Results

Unplanned Downtime

Unplanned equipment failures reduced from 340 to 221 hours annually

Prediction Horizon

Average advance warning before equipment failure event

Prediction Precision

True positive rate on equipment failure predictions (held-out test)

Annual Savings

Emergency repair cost reduction in first year of operation

How We Delivered It

IoT Data Pipeline

Configured AWS IoT Core to ingest 8,000 sensor streams with MQTT protocol, routing to Kinesis Firehose → Timestream.

Feature Engineering

Extracted 34 time-series features (rolling statistics, FFT frequencies, thermal gradients) from raw sensor data.

LSTM Model Development

Trained TensorFlow LSTM on 2 years of sensor data with failure labels. Achieved AUC-ROC of 0.94 on held-out equipment.

Real-Time Inference Pipeline

Deployed SageMaker endpoint scoring each asset every 30 minutes. Failure risk scores written to Timestream for dashboarding.

Grafana Dashboard

Built asset health dashboard with interactive failure prediction timeline, risk ranking table, and historical anomaly viewer.

PagerDuty Alert Integration

Configured tiered alert routing: 48-hour warnings to maintenance scheduler, 12-hour warnings to on-call engineers.

"We went from reactive fire-fighting to planned interventions. Our maintenance team now has a 48-hour window to schedule every repair — the operational difference is transformational."
Head of Asset Management
Energy Infrastructure Operator

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