#  GHI Prediction — Solar Radiation Forecasting System

This interactive demo presents a production-grade **machine learning system** for predicting **Global Horizontal Irradiance (GHI)** using real-world weather data collected from weather stations across **Saudi Arabia**.

The model enables fast and accurate solar radiation forecasting, supporting applications in **solar energy planning, grid optimization, and sustainable infrastructure development**.

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##  Input Features & Valid Ranges

All inputs are validated using real data bounds to ensure reliable predictions:

| Feature | Valid Range |
|-------|------------|
Air Temperature (°C) | 8.10 – 39.70 |
Air Temperature Uncertainty (°C) | 0.50 – 0.60 |
Wind Direction (°N) | 0 – 359 |
Wind Direction Uncertainty (°N) | 0.0 – 4.80 |
Wind Speed at 3m (m/s) | 0.0 – 7.80 |
Wind Speed Uncertainty (m/s) | 0.0 – 0.10 |
Wind Speed Std Dev (m/s) | 0.0 – 4.50 |
DHI (Wh/m²) | 865.8 – 6394.6 |
DHI Uncertainty (Wh/m²) | 37.3 – 1344.8 |
Standard Deviation DHI (Wh/m²) | 148.3 – 1649.6 |
DNI (Wh/m²) | 0.3 – 9517.0 |
DNI Uncertainty (Wh/m²) | 0.2 – 2115.8 |
Standard Deviation DNI (Wh/m²) | 0.4 – 3282.2 |
GHI Uncertainty (Wh/m²) | 98.4 – 5249.6 |
Standard Deviation GHI (Wh/m²) | 117.6 – 1720.7 |
Peak Wind Speed at 3m (m/s) | 0.0 – 40.5 |
Peak Wind Speed Uncertainty (m/s) | 0.0 – 0.20 |
Relative Humidity (%) | 8.3 – 95.9 |
Relative Humidity Uncertainty (%) | 2.8 – 3.7 |
Barometric Pressure (hPa) | 831.3 – 1019.7 |
Barometric Pressure Uncertainty (hPa) | 4.1 – 6.2 |

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##  Model & Training Summary

Multiple machine learning models were trained and evaluated:

- Linear Regression **(Deployed Model)**
- Random Forest
- Decision Tree
- KNN
- SVR
- XGBoost
- Artificial Neural Network
- Histogram Gradient Boosting

###  Final Model Selection Strategy

The final deployed model — **Linear Regression** — was selected using an automated ranking system that balances:

- **Prediction accuracy** (RMSE, R²)
- **Inference latency** (real-time performance)

This approach ensures both **high accuracy and fast response** for real-world deployment.

###  Best Model Performance

| Model | MAE | RMSE | R² | Training Time |
|------|------|------|------|------|
Linear Regression | 135.94 | 193.74 | 0.97 | 0.01s |
Histogram Gradient Boosting | 140.58 | 192.11 | 0.98 | 0.83s |
XGBoost | 148.37 | 198.05 | 0.97 | 2.73s |
ANN | 154.4 | 241.22 | 0.96 | 10.6s |

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##  Experiment Tracking

All experiments, comparisons, and model selection were tracked using **Weights & Biases** with full reproducibility and visualization.

🔗 W&B Dashboard:  
https://wandb.ai/nishnarudkar-d-y-patil-university/solar-radiation-prediction

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##  Full Project Ecosystem

- 🖥️ **Live Web Application:**  
  https://solar-radiation-prediction-using-saudi.onrender.com
- 💻 **Source Code (GitHub):**  
  https://github.com/nishnarudkar/Solar-Radiation-Prediction-using-Saudi-Arabia-Dataset
- 🧑‍🔬 **AWS Builder Center Article:**  
  https://builder.aws.com/content/36qXvwTipuNwbZMUPf3hQ7n2IS0/from-experiment-tracking-to-automated-model-selection-a-solar-radiation-prediction-pipeline
- 📰 **Medium Article:**  
  https://medium.com/@nishnarudkar/from-experiment-tracking-to-automated-model-selection-a-practical-ml-workflow-cf193d1b1098

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##  Team

- **Nishant Narudkar**
- Maitreya Pawar  
- Vatsal Parmar  
- Aamir Sarang  

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This Hugging Face Space demonstrates the final trained model with validated real-world constraints and a clean interactive interface for public experimentation.