Solar Panel AI Diagnostics

TechTrident | Wadla 4.0 2025 | HF Demo

Problem Statement 3: Solar Panel Maintenance

AI system detects defects/degradation (cracks, hotspots, soiling) using tabular performance data + images. ONNX export required. Public datasets only.

Classes: ['Bird-drop', 'Clean', 'Dusty', 'Electrical-damage', 'Physical-Damage', 'Snow-Covered']

Architecture

System Workflow

Use Case Diagram

Models: ResNet18 + XGBoost + RandomForest | ONNX v1.15

Dependencies Installation

pip install -r requirements.txt

Outputs: resnet18_solar.onnx, xgboost_degr.onnx

Output: {"priority": "HIGH", "30d_loss": "18.2%"}

ONNX Inference

cd code
python infer.py

Live Demo: HuggingFace Space

Quick Results

Metric	Value
Defect Acc	99.2%
Degradation RMSE	0.87%
Inference	15ms

Structure

/model
   └── final_model.onnx
/code
   ├── training.ipynb
   └── infer.py
/data
   └── dataset_info.txt
/logs
   └── training_logs.txt
README.md
requirements.txt

Team TechTrident

• Lead ML Engineer: Dev Kumar Sharma (Kuch aur Train krna hai Model)
• Computer Vision: Abhay Gupta
• Full-Stack Dev: Aditya Patwa
• BTech CS, Shri Ram IT, Jabalpur

Contact: contact2abhay@gmail.com

Datasets: PV Panel Defect Dataset TechTrident | Wadla 4.0 Hackathon 2025