Predictive Maintenance with AI: Reducing Downtime in Power Equipment

Avoid costly equipment failures by applying AI to maintenance strategies. This course enables energy professionals to use sensor data and analytics to predict breakdowns and schedule smarter interventions.

Course Description:

This 2-day course is designed to empower renewable energy planners, analysts, and development teams with the foundational knowledge to apply AI for smarter project siting, resource forecasting, and integration planning. As the global energy transition accelerates, renewables must not only be clean but also data-smart—and AI is a key enabler of this evolution.

Participants will explore how AI is being used in solar, wind, and hybrid energy systems to predict production, improve site selection, assess financial feasibility, and support grid harmonization. Through real-world case studies, tool demonstrations, and guided exercises, attendees will gain practical skills to enhance their planning accuracy, reduce project risk, and maximize ROI—without requiring advanced technical or coding expertise.

Who Should Attend:

• Renewable Energy Planners (solar, wind, hybrid)
• Energy Analysts and Project Developers
• Power System Planners & Clean Energy Consultants
• Environmental Engineers and Energy Economists
• Sustainability/Net-Zero Strategy Teams

Course Objectives:

Knowledge Acquisition:

• Understand how AI contributes to renewable energy planning and forecasting
• Learn key AI models used for siting, weather prediction, and generation modeling
• Explore the role of AI in financial feasibility and environmental analysis
• Grasp the limitations and ethical considerations of data-driven decision-making in renewables

Skills Development:

• Evaluate and interpret AI-generated resource maps and forecasts
• Identify optimal project sites using AI-supported geospatial tools
• Analyze generation potential and grid impact scenarios with AI models
• Use AI for environmental sensitivity analysis and planning trade-offs
• Simulate ROI based on AI-forecasted generation vs. actual demand

Practical Application:

• Build a site screening matrix for a solar/wind project using AI data
• Simulate generation output using AI-based weather prediction tools
• Develop a planning strategy that balances cost, location, and capacity risk
• Create a report using AI-supported planning visuals for investor/stakeholder engagement
• Conduct a mock case study on hybrid energy optimization

What will I Learn From it:

• How to use AI to improve site selection and resource quality assessments
• Tools to forecast solar/wind production with higher precision
• Ways to integrate environmental constraints into energy planning
• How AI helps in matching project output with grid constraints and market needs
• Build stronger cases for renewable investments using intelligent data modeling

Training Methodology

• Case-Based Teaching: Real-life applications from leading solar and wind markets
• Interactive Tool Demonstrations: AI platforms for resource mapping, forecasting, and LCOE modeling
• Guided Team Exercises: Structured problem-solving on mock projects
• Capstone Projects: Collaborative group presentations to consolidate learning
• Visual Learning: Dashboards, heatmaps, and scenario modeling to aid understanding

Requirement/
pre-requisites:

• Background in renewable energy, energy economics, engineering, or project development
• Familiarity with resource assessments or project feasibility basics
• No coding or AI technical knowledge required
• Bring a laptop (Excel or browser-based tool use required for exercises)