Context
Improving productivity across a renewable energy asset portfolio increasingly depends on better planning and coordination of operations and maintenance activities.
Challenge
O&M decisions require balancing multiple, changing inputs (weather, asset condition, workforce capacity, supply constraints, and market signals), which makes it difficult to consistently choose the optimal maintenance actions.
AI Solution
AI agents integrate weather forecasts, asset health data, workforce availability, supply chain constraints, and market signals to first recommend, and then automate, optimal O&M decisions. The tool was developed to enhance O&M for hydroelectric assets and is now being deployed in another geography.
Impact & evidence
The tool has demonstrated that AI can enhance predictive maintenance results, including in complex systems, and is being deployed beyond the initial development environment.
Implementation / Adoption
The solution integrates multiple operational inputs, including weather forecasts, asset health data, workforce availability, supply chain constraints, and market signals, to support O&M decision‑making.
Data
Data sources include weather forecasts, asset health data, workforce availability, supply chain constraints, and market signals.
Future enhancements
The solution is intended to evolve from recommendation of optimal O&M decisions to automation of those decisions.