Predictive Maintenance for a Green Energy Producer

Predictive Maintenance for a Green Energy Producer

Customer Overview

• Company Logo: [Anonymized]
• Company Name: Undisclosed at client request
• Domain/Industry: Renewable energy generation (wind/solar), Northern Europe
• Short Description: An independent green energy producer operating a distributed fleet of wind and solar generation assets across multiple sites, facing rising unplanned maintenance costs as the fleet scaled.

Challenge

Equipment failures across distributed generation assets were detected reactively, often after output had already dropped. A single turbine or inverter outage disrupted forecasted output and triggered costly emergency dispatches, with no unified view of asset health across the fleet.

Solution

Grid Dynamics built a predictive maintenance platform that ingests SCADA and sensor telemetry through Kafka and Airflow pipelines, applies ML-based failure-risk scoring per asset, and surfaces maintenance recommendations before failures occur, integrated into the client's existing field service workflows.

Results

• 27% reduction in unplanned asset downtime across the monitored fleet
• 19% decrease in emergency maintenance dispatches within the first two quarters
• Maintenance scheduling shifted from reactive to risk-ranked and proactive across all monitored sites

Tech Stack

Python, Apache Kafka, Airflow, PostgreSQL, ML-based predictive maintenance models, Google Cloud Platform / Azure, SCADA integration

Delivery Highlights

• Team size: 8 engineers
• Duration: 5 months to first production use case, expanded fleet-wide over 3 additional months

Outcome

• 27% reduction in unplanned downtime
• 19% fewer emergency dispatches, directly reducing field service cost
• Payback on platform investment within approximately 9 months