Problem
Operators of wind and solar farms increasingly face challenges related to unstable energy production and unexpected equipment failures, leading to significant financial losses. There is a lack of tools for accurately predicting faults in wind turbines, PV panels, or biogas engines. Manual data monitoring across dozens of locations is inefficient, resulting in rising O&M (operation and maintenance) costs and lost production.
Goal
To improve the reliability and efficiency of RES (renewable energy sources) farms by implementing AI to predict failures and optimize energy generation based on IoT data and weather forecasts.
Story
A hybrid wind and solar farm operator in Central and Eastern Europe sought a solution to reduce unplanned turbine downtime and improve production stability. The key challenges were highly variable performance, manual reporting, and a lack of failure prediction, which led to costly emergency maintenance operations.
To address this, the operator implemented an AI system that analyzed data from IoT sensors installed on wind turbines and PV panels — including bearing temperature, vibration, humidity, voltage, blade angle, and inverter performance. Machine learning algorithms, integrated with SCADA and EMS systems, detected failure patterns and generated early alerts for maintenance teams, enabling preventive action.
The system also used weather forecasts (wind, solar irradiance) to dynamically optimize production — adjusting turbine power curves and PV tracker angles in real time. This allowed for higher energy yields without additional infrastructure investment.
In the next phase, the operator plans to integrate with energy trading platforms and extend predictive analytics to include battery storage systems — further automating processes and improving power balancing.
Results (KPI)
Thanks to the AI implementation, the operator achieved:
✅ 45% reduction in equipment failures
✅ 25% lower maintenance costs (approx. 220,000 EUR annually)
✅ 8% increase in energy yield without expanding infrastructure
✅ Shortened maintenance response times from days to hours
✅ Full ROI in 2.5 years — driven by O&M savings and higher energy output
🌱 Optimized energy production increased the share of green energy in the mix and reduced losses — supporting the operator’s ESG goals
💡 The implementation improved the company’s competitive position in tenders and enhanced its standing with energy sector investors
Implementation Summary
- Installation of IoT sensors on wind turbines, PV panels, and biogas systems
- Integration of SCADA, EMS, and weather forecast data
- Training ML models for failure prediction and equipment optimization
- Automated alerts and integration with the service ticketing system
- KPI reporting and full data visualization on dashboards — used by O&M teams and technical leadership for operational decision-making
Scope of Implementation
- Predictive maintenance: AI analyzing sensor data to forecast failures
- Production optimization: Dynamic control of energy output based on forecasts and real-time data
- Integration with EMS and SCADA: Unified analysis of production and operational data
- Edge computing: Local data processing closer to the devices
📦 The solution was deployed in a hybrid model (local infrastructure + cloud components), with the ability to scale across additional sites without infrastructure changes.
Technology Stack – Core and Supporting
⚙️ Artificial Intelligence (AI): Machine learning used to forecast failures and fine-tune turbine and PV panel operations
⚙️ Internet of Things (IoT): Sensors measuring temperature, vibration, voltage, humidity
⚙️ Edge computing: Local data processing (e.g. Nvidia Jetson, AWS Greengrass), enabling real-time anomaly detection and minimizing latency
⚙️ Predictive Analytics: Pattern analysis and consumption trend forecasting
⚙️ EMS/SCADA Integration: Real-time control and analysis of production system data
