AI-Driven Optimization for Solar & Thermal Energy Systems
Harness AI to Maximize Solar Performance, Prevent Failures, and Optimize Efficiency
Skills you will gain:
About Workshop:
This 3-day hands-on workshop empowers participants to apply AI techniques to forecast solar and thermal system performance, detect faults, and optimize energy efficiency. Participants will build predictive models for site selection and performance forecasting, develop fault detection systems using sensor data, and create digital twin simulations for real-time performance and degradation analytics. Each day includes practical exercises with clear deliverables—performance models, fault detection systems, and optimization insights.
Aim: 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.
Workshop Objectives:
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Predict the performance of floating solar and solar thermal systems using AI models.
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Build site selection models considering environmental and geographic factors.
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Detect system faults early and predict maintenance needs using AI and sensor data.
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Create digital twin models to simulate system behavior and optimize energy efficiency.
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Analyze degradation and apply AI for long-term performance prediction.
What you will learn?
📅 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
Mentor Profile
Fee Plan
Important Dates
26 Jan 2026 Indian Standard Timing 4 PM
26 Jan 2026 to 28 Jan 2026 Indian Standard Timing 5: 30 PM
Get an e-Certificate of Participation!
Intended For :
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Students, researchers, and professionals in Renewable Energy, Electrical Engineering, AI, or related fields.
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Basic Python knowledge is required (scikit-learn, data handling).
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No prior experience with digital twins or predictive maintenance necessary (workshop provides full guidance).
Career Supporting Skills
Workshop Outcomes
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Develop AI models for forecasting solar system performance using environmental data (Python, scikit-learn).
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Build site selection models and generate recommendations for optimal solar system placement.
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Train predictive maintenance models and detect faults in solar/thermal systems.
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Construct a fault detection system and apply it to real-world sensor data.
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Design digital twin models to simulate and optimize solar system performance.
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Extract insights on degradation and real-time optimization for improved system efficiency.