Galactica Solar Inspection is an intelligent solar energy monitoring platform designed to streamline inspection, analysis, and maintenance operations of solar photovoltaic (PV) panel installations. Built using Django and Python, the platform serves as the digital backbone for real-time performance tracking and fault detection, improving operational efficiency in solar farms or rooftop installations.

The solar energy industry faces several challenges when it comes to maintaining peak panel efficiency and ensuring rapid issue resolution, such as:

• Manual inspections are labor-intensive and slow.
• Inefficient fault detection leads to energy losses.
• Lack of historical data analytics makes it hard to optimize panel layout or detect recurring problems.
• Difficult coordination between technicians, operators, and engineers on large-scale sites.

1. Dashboard & Visualization

   • Real-time status of solar panels (e.g., output, temperature, degradation).
   • Health check summaries and performance trends over time.

2. Automated Inspection Scheduling

   • Trigger inspections based on anomaly scores, time intervals, or maintenance logs.
   • Calendar-based view for scheduled and completed inspections.

3. Fault Detection Engine

   • Automatically analyze data (or drone imagery) to detect common PV issues such as:

     • Hotspots
     • Cell degradation
     • Dirt accumulation
     • Shading anomalies

4. Reporting & Notifications

   • Generate inspection reports.
   • Email/SMS alerts to technicians or admins.

5. User Roles & Access

   • Role-based access for Admins, Inspectors, Engineers, and Operators.

6. Audit Logging

   • Detailed logs for every action, change, or inspection.

• Type: Supervised Learning (Classification)
• Input: Image features, sensor telemetry (temperature, voltage, current)
• Output: Fault category (hotspot, shading, dirt, etc.)
• Models: Random Forest / SVM / CNN (if image-based)

• Type: Unsupervised Learning / Rule-Based
• Input: Time-series panel performance data
• Output: Anomaly score per panel
• Algorithms: Isolation Forest, Z-score thresholds, Autoencoders

• Type: Time Series Forecasting
• Input: Maintenance logs, usage patterns, panel metadata
• Output: Remaining Useful Life (RUL), probability of failure
• Algorithms: ARIMA, LSTM (long short-term memory networks)

• Input: Thermal/Visual imagery from drones
• Output: Fault heatmaps over the panel array
• Techniques: Object Detection (YOLO, Faster R-CNN), Image Segmentation

• ✅ Django REST Framework: For building APIs consumed by mobile/web frontends.
• ✅ Celery + Redis: Task queue system for long-running background jobs (e.g., image analysis, report generation).
• ✅ PostgreSQL / SQLite: Backend database support.
• ✅ OpenCV / PIL / PyTorch / TensorFlow: Image processing and model inference (if image-based inspection is implemented).

• HTTPS (SSL) for all web interactions
• Token-based authentication for APIs
• Audit logs for all database write operations
• Role-based permissions for sensitive actions

• 🌩 Integration with IoT sensors and real-time stream data
• 🌍 Geo-mapping panel arrays with interactive views
• 📱 Mobile application for on-field inspection and QR-based scanning
• ☁️ Cloud-native deployment (Docker, Kubernetes, AWS/GCP)