Context
Optimising the performance of renewable portfolios requires continuous monitoring and timely operational decisions across diverse asset types (e.g., wind, solar, and storage).
Challenge
Renewable operators face unplanned equipment failures, difficulty detecting anomalies early, and high downtime due to inefficient maintenance planning.
AI Solution
CGI RMS enables real‑time monitoring, predictive maintenance, and performance analytics across renewable portfolios.
The solution uses:
- Machine‑learning models (Azure ML integration) to detect anomalies and predict failures
- Health index and status indicators to assess asset reliability
- Regression and classification models for failure prediction and deviation detection
- Digital twin‑like comparisons to compare expected vs actual performance
These capabilities support early fault detection, time‑to‑failure prediction, and root‑cause diagnosis.
Impact & evidence
Impact is evidenced through KPIs tracked across the renewable portfolio, including availability (%), energy production versus budget, mean time between failures (MTBF), mean time to repair (MTTR), and energy losses and efficiency KPIs.
Implementation / Adoption
Implementation requires the availability of SCADA data across wind, PV, and BESS assets, supported by reliable communication infrastructure and strong data quality and consistency.
Deployment typically follows a phased approach, starting with a pilot of approximately 6–8 weeks, followed by a minimum implementation period of around 4–5 months, with full rollout scalable across the portfolio, subject to integration with OEM systems and the resolution of data gaps or quality issues.
Data
The solution requires data on SCADA (wind, solar, BESS), Weather (wind speed, irradiance), operational and maintenance, and market and budget data.
Constraints:
Data gaps or poor quality may require correction tools
Integration with OEM systems required
Future enhancements
Planned future enhancements include advanced AI models and digital twins, ESG and sustainability reporting, autonomous O&M capabilities and AI copilots for operators.
