About Workshop
Microclimate Simulation under Solar Panels – AI-Enabled Models for Temperature, Humidity, and Light Optimization” is a 3-day practical workshop that teaches how to build, evaluate, and deploy simple AI models to simulate and optimize microclimate conditions in solar energy systems, such as agrivoltaic farms.
Participants will explore data loading, basic preprocessing, simulation modeling, rule-based and machine learning approaches, and optimization techniques. Case studies from real-world solar farms, weather monitoring, and environmental datasets will be integrated for hands-on experience using guided Python notebooks.
Aim
To train participants in designing and applying AI models for simulating microclimate under solar panels, optimizing factors like temperature, humidity, and light to enhance energy production, crop growth, and system efficiency in sustainable solar deployments.
What Participants Will Learn
Build simple AI models for microclimate simulation under solar panels
Quantify and visualize optimizations for temperature, humidity, and light
Analyze and interpret solar microclimate datasets using beginner-friendly tools
Deploy basic simulations and visualizations for project planning
Structure
📅 Day 1 – Foundations of Microclimate & Data Acquisition
- Microclimate Basics (Temperature, Humidity, Light under Solar Panels) and Shading Effects
- Discussions about the Dataset chosen
- Exploratory Data Analysis and Visualization of Environmental Trends
📅 Day 2 – AI Modeling for Microclimate Simulation
- Basic Simulation Techniques
- Feature Engineering for Shading and Optimization
- Model Evaluation
- Tools: Python, NumPy, Pandas, Scikit-learn, Matplotlib
📅 Day 3 – Optimization & Deployment
- Optimizing Light, Temperature, and Humidity for Solar Efficiency
- Scenario Simulations
- Linking Models to Real-World Insights
- Visualization & Simple Apps
Important Dates
Registration Ends
4:30
Workshop Dates
2025-09-16
5:30
5:30
What You Will Gain
Who Should Attend
Renewable energy engineers and solar system designers
Data analysts and beginners in environmental AI
AI/ML enthusiasts working in sustainability and agriculture
Government solar policy officers and urban planners
Graduate students and researchers in solar energy or climate modeling