| # 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**. | |
| --- | |
| ## 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 | | |
| --- | |
| ## 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 | |
| --- | |
| ## 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 | |
| --- | |
| ## Team | |
| - **Nishant Narudkar** | |
| - Maitreya Pawar | |
| - Vatsal Parmar | |
| - Aamir Sarang | |
| --- | |
| This Hugging Face Space demonstrates the final trained model with validated real-world constraints and a clean interactive interface for public experimentation. | |