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.

• 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.