Optimizing Renewable Energy Projects with AI: Smarter Siting, Forecasting & Grid Readiness

Plan and execute renewable energy projects more effectively using AI-powered tools. This course helps you optimize site selection, forecasting, and financial modeling to maximize generation and returns.

Course Description:

This 2-day course equips maintenance engineers, reliability managers, and operations leads in the power sector with the essential skills to leverage AI for predictive maintenance. From substations to turbines, unplanned outages cost millions in lost energy, downtime, and penalties. With the rise of sensors and digitized asset logs, AI now allows teams to move from reactive or time-based maintenance to condition-based and predictive maintenance strategies.

Participants will learn how AI models use data to detect anomalies, predict equipment failure, and generate maintenance recommendations. Real-world case studies from power plants, transmission networks, and renewable installations provide context for applying these tools—without requiring coding or complex model-building skills.

Who Should Attend:

• Maintenance Engineers in Power Generation & Transmission
• Reliability Engineers and Condition Monitoring Technicians
• O&M Managers (Thermal, Hydro, Wind, Solar)
• Substation and Switchgear Asset Managers
• Digital Transformation Leads in Energy Utilities

Course Objectives:

Knowledge Acquisition:

• Understand how predictive maintenance differs from preventive and reactive strategies
• Learn AI techniques for anomaly detection, failure forecasting, and sensor data analysis
• Explore how leading energy firms use AI for asset reliability and maintenance optimization
• Understand the data types and infrastructure needed for AI success

Skills Development:

• Interpret AI-powered diagnostics and RUL (Remaining Useful Life) reports
• Build failure risk profiles for key assets
• Use visual dashboards to identify high-risk equipment
• Create priority-based maintenance schedules using AI insights
• Structure a condition monitoring framework for critical systems

Practical Application:

• Analyze mock sensor data from transformers, turbines, and batteries
• Generate a predictive maintenance calendar based on AI output
• Compare cost impacts of predictive vs. traditional maintenance cycles
• Evaluate asset health scores and plan interventions
• Present a mini-case with predictive ROI justification

What will I Learn From it:

• Reduce downtime and maintenance costs using AI forecasts
• Extend equipment life and avoid catastrophic failures
• Detect issues early before human inspections would catch them
• Create smarter maintenance routines based on asset conditions
• Justify predictive programs with business case logic and dashboards

Training Methodology

• Real-World Case Studies: Based on actual power utility applications
• Tool Demonstrations: Dashboards, RUL visualizations, data trend tracking
• Collaborative Team Work: Capstone planning and data interpretation
• Hands-On Exercises: Use simplified data sets for diagnostics and scheduling
• Templates & Playbooks: Action plans, diagnostic models, and inspection flows

Requirement/
pre-requisites:

• Familiarity with power generation or transmission asset classes
• Basic understanding of maintenance cycles and reliability metrics
• Laptop with spreadsheet capability (Excel or Google Sheets)
• No AI or programming knowledge required