Predictive Maintenance (ML) for Solar Parks — research collaboration to reduce failures

Client situation

• Asset-heavy operations where failures cause cost, downtime, and service disruption
• Objective: detect patterns early and forecast maintenance needs
• Need for data-driven decision support to optimize maintenance scheduling

Solar parks generate continuous streams of sensor telemetry — irradiance, temperature, inverter output, and string-level performance — but without pattern detection this data goes underused. Maintenance teams were reacting to failures rather than preventing them, which increased downtime and spare-part costs. The collaboration with a PhD researcher provided both domain knowledge on photovoltaic assets and the machine learning methodology to build a structured predictive approach.

What was delivered

• A machine learning-based predictive maintenance system for solar parks
• Implemented together with a PhD researcher in a research cooperation (2020)
• Pattern detection and failure forecasting capabilities

The delivered system ingested historical sensor data alongside maintenance records and environmental signals to train models capable of identifying failure precursors. Explainable outputs — prioritized work order recommendations and outage risk scores — were designed so that operations teams, not data scientists, could act on the results. An MLOps framework was defined to support drift detection and model retraining as new data accumulated over time.

Governance approach

The delivery followed a security-by-design and governance-first approach:

• ITILv4-aligned development processes
• ISMS / ISO 27001-oriented controls for data handling
• Documentation-first delivery with operational handover

Outcome

• Improved ability to anticipate maintenance needs and failures early
• Data-driven prioritization of maintenance activities
• Foundation for scaling predictive capabilities to additional asset types

The research cooperation produced a validated prototype demonstrating that pattern detection and failure forecasting were feasible with the available sensor and maintenance data. The documentation-first handover ensured the methodology could be understood, extended, and adapted by the operations team independently.

Scope & Limitations

This engagement was a research cooperation scoped as a proof-of-concept system. Production deployment, ongoing model hosting, real-time alerting infrastructure, and integration with existing SCADA or ticketing systems were explicitly out of scope. The work established feasibility and a replicable methodology — not a fully productionized MLOps pipeline. Organizations seeking production-grade predictive maintenance systems would require a separate implementation and operationalization phase building on this foundation.