AI-Driven Optimization for Solar & Thermal Energy Systems

To train participants in using AI for optimizing solar and thermal energy systems, covering performance prediction, fault detection, predictive maintenance, and real-time optimization via digital twins.

• Predict the performance of floating solar and solar thermal systems using AI models.

• Build site selection models considering environmental and geographic factors.

• Detect system faults early and predict maintenance needs using AI and sensor data.

• Create digital twin models to simulate system behavior and optimize energy efficiency.

• Analyze degradation and apply AI for long-term performance prediction.

📅 Day 1 — AI for Performance Prediction and Site Selection

• AI for forecasting performance of floating solar and solar thermal systems
• Site selection modeling: key environmental and geographic factors
• Hands-on: Build an AI model to predict system performance based on environmental variables using Python (scikit-learn)
• Deliverable: Performance prediction model and site selection recommendations

📅 Day 2 — Predictive Maintenance and Fault Detection

• AI methods for detecting system faults early and predictive maintenance strategies
• Remote sensing and sensor integration for fault detection
• Hands-on: Train a predictive maintenance model using sensor data to identify system faults
• Deliverable: Fault detection system and maintenance prediction model

📅 Day 3 — Digital Twin, Degradation Analytics, and Efficiency Optimization

• Introduction to digital twin technology for simulating system behavior
• Degradation analytics for predicting long-term system performance
• AI for optimizing energy efficiency in real-time
• Hands-on: Build a digital twin model to simulate and optimize the performance of a floating solar system
• Deliverable: Digital twin simulation and efficiency optimization insights

• Students, researchers, and professionals in Renewable Energy, Electrical Engineering, AI, or related fields.

• Basic Python knowledge is required (scikit-learn, data handling).

• No prior experience with digital twins or predictive maintenance necessary (workshop provides full guidance).

• Develop AI models for forecasting solar system performance using environmental data (Python, scikit-learn).

• Build site selection models and generate recommendations for optimal solar system placement.

• Train predictive maintenance models and detect faults in solar/thermal systems.

• Construct a fault detection system and apply it to real-world sensor data.

• Design digital twin models to simulate and optimize solar system performance.

• Extract insights on degradation and real-time optimization for improved system efficiency.