Rising O&M costs are straining renewable energy economics, especially offshore wind. See how predictive maintenance using IoT, digital twins, and AI helped a European operator cut costs by 11%—unlocking $8M+ in annual savings.
Operations and maintenance (O&M) costs remain one of the most significant constraints on renewable energy economics, particularly for offshore wind. With maintenance consuming up to 38% of operating budgets, renewable operators are under pressure to improve efficiency without compromising reliability or safety.
Traditional maintenance models rely on scheduled inspections and reactive repairs, resulting in unnecessary site visits, costly emergency interventions, and extended production downtime. These inefficiencies directly impact project returns and slow renewable capacity expansion.
Predictive Maintenance Changes the Equation
Advances in IoT, digital twins, and machine learning are enabling a shift from reactive to predictive, condition-based maintenance. By continuously analyzing sensor data from turbines and other assets, operators can detect early signs of degradation and intervene before failures occur.
To fully capitalize the value of predictive maintenance, a leading European renewable energy operator partnered with FPT to deploy this technology across their onshore and offshore wind portfolio, integrating sensor data with cloud-based analytics to monitor asset health in real time.
Measured Results Within 18 Months
- 11% reduction in total costs, translating to over $8 million in annual savings
- 60% reduction in field inspections, replacing routine visits with remote monitoring
- 85% faster repair response times through proactive planning and logistics optimization
- 3–5% increase in annual energy production through data-driven operational tuning
- Extended asset lifespans and deferred capital replacement investments
Autonomous drone inspections further enhanced results, delivering high-precision defect detection faster and more safely than manual inspections.
Enabling Renewable Scale-Up
As predictive maintenance becomes an industry standard, it is expected to be adopted across a majority of European wind farms by 2027. This trend is enabling renewable operators to accelerate capacity expansion while managing operational risk more effectively. For Germany’s goal of achieving 80% renewable electricity by 2030, data-driven asset intelligence will be a critical enabler.
Meet FPT at E-World 2026, Booth 5H102, to discover how predictive analytics, digital twins, AI, and cloud platforms can help renewable operators reduce O&M costs, improve asset performance, and scale clean energy sustainably.
Source:
https://samotics.com/blog/reduce-maintenance-costs-for-your-wind-farm
https://www.bundeswirtschaftsministerium.de/Redaktion/EN/Dossier/renewable-energy.html