README.md

metadata

title: Solar PV Predictive Maintenance
emoji: ☀️
colorFrom: blue
colorTo: gray
sdk: docker
app_port: 7860
pinned: false

Solar PV Predictive Maintenance API

AI-powered predictive maintenance for solar PV inverters using ML models and LLM-based diagnosis.

Features

• Anomaly Detection: Isolation Forest + LSTM Autoencoder
• Failure Forecasting: XGBoost models for time-to-failure and failure probability
• AI Diagnosis: Gemini LLM provides maintenance recommendations
• REST API: FastAPI-based service with automatic documentation

Quick Start

Installation

git clone <repository-url>
cd pdm-agent
pip install -r requirements.txt

Environment Setup

Set your Google API key for LLM features:

export GOOGLE_API_KEY=your_api_key_here

Run

uvicorn app:app --host 0.0.0.0 --port 7860 --reload

API Usage

POST /analyze

Analyzes sensor data and returns ML predictions with optional AI diagnosis.

Request:

{
  "vdc1": [600.0, 601.0, 602.0],
  "idc1": [10.0, 10.1, 10.2],
  "api_key": "your_google_api_key_here",
  "asset_id": "PV_INVERTER_001"
}

Parameters:

• vdc1, idc1: Voltage and current sensor readings
• api_key: Optional Google API key for AI diagnosis
• asset_id: Optional asset identifier (auto-generated if not provided)

Response:

{
  "ml_output": {
    "asset_id": "PV_INVERTER_001",
    "failure_probability": 0.12,
    "expected_ttf_days": 450.5,
    "expected_rul_days": 9800.0,
    "confidence": 0.85
  },
  "agent_output": {
    "diagnosis": "Minor voltage fluctuations detected...",
    "urgency": "Low",
    "recommended_action": "Continue monitoring...",
    "justification": ["Voltage within range", "Current stable"]
  }
}

Data Processing Pipeline

1. Input Validation: Ensures voltage/current arrays match and contain sufficient data points
2. Data Preparation: Pads input to 100 data points for consistent processing
3. Feature Engineering: Creates 7 statistical features using rolling window analysis:
   • Voltage mean/standard deviation
   • Power mean/standard deviation
   • Power delta and slope
   • Normalized efficiency
4. ML Inference: Processes features through anomaly detection and prediction models
5. Agent Analysis: LLM analyzes ML results for human-readable diagnosis (if API key provided)

Configuration

• GOOGLE_API_KEY: Required for AI diagnosis features
• Models: Gemini 2.5 Flash Lite, XGBoost, LSTM Autoencoder
• Port: 7860 (configurable)

Docker

docker build -t pdm-agent .
docker run -p 7860:7860 -e GOOGLE_API_KEY=your_key pdm-agent

Testing

# Test the API
curl -X POST "http://localhost:7860/analyze" \
  -H "Content-Type: application/json" \
  -d '{
    "vdc1": [600.0, 601.0, 602.0],
    "idc1": [10.0, 10.1, 10.2],
    "api_key": "your_api_key",
    "asset_id": "PV_INVERTER_001"
  }'

📊 ML Models

Anomaly Detection

• Isolation Forest: Unsupervised outlier detection
• LSTM Autoencoder: Sequence-based anomaly scoring

Predictive Models

• XGBoost Classifier: Failure probability prediction
• XGBoost Regressor: Time-to-failure estimation

LLM Agent

• Gemini 2.5 Flash Lite: Diagnostic reasoning and recommendations

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

MIT License