---
library_name: sklearn
tags:
- energy-consumption
- regression
- random-forest
- xgboost
- building-energy
- sustainability
- carbon-footprint
pipeline_tag: tabular-regression
---

# Ecologia Gas Consumption Model

## Model Description

This model predicts **gas_consumption (m³)** for buildings using machine learning ensemble methods.

- **Model Architecture**: Random Forest Regressor (Best Model)
- **Task**: Regression (Energy Consumption Prediction)
- **Target Variable**: gas_consumption (m³)
- **Input Features**: 22 features
- **Training Dataset**: Building Data Genome Project 2
- **Training Samples**: ~15 million

## Model Performance

### Random Forest Model
- **RMSE**: 459.7374
- **MAE**: 131.9079
- **R² Score**: 0.9090

### XGBoost Model
- **RMSE**: 499.6148
- **MAE**: 156.0127
- **R² Score**: 0.8925

### Best Model
The best performing model (based on validation RMSE) is saved as `gas_model.joblib`.

## Training Details

### Dataset
- **Source**: [Building Data Genome Project 2](https://www.kaggle.com/datasets/claytonmiller/buildingdatagenomeproject2)
- **Training Samples**: ~15 million
- **Data Preprocessing**: 
  - Outlier removal (99th percentile)
  - Feature engineering (temporal, building, weather features)
  - Missing value imputation
  - Normalization

### Training Method
- **Algorithm**: Ensemble (Random Forest + XGBoost)
- **Best Model Selection**: Based on validation RMSE
- **Cross-Validation**: Train/Validation/Test split (60/20/20)
- **Hyperparameters**: Optimized for large-scale datasets

### Feature Engineering
The model uses 22 engineered features including:
- **Building Features**: Type, area, age, location
- **Temporal Features**: Hour, day, month, season, day of week
- **Weather Features**: Temperature, humidity, dew point
- **Interaction Features**: Building-weather interactions
- **Lag Features**: Previous consumption patterns

## Usage

### Installation
```bash
pip install scikit-learn xgboost joblib huggingface_hub
```

### Load Model
```python
from huggingface_hub import hf_hub_download
import joblib

# Download model and features
model_path = hf_hub_download(
    repo_id="codealchemist01/ecologia-gas-model",
    filename="gas_model.joblib",
    token="YOUR_HF_TOKEN"  # Optional if public
)

features_path = hf_hub_download(
    repo_id="codealchemist01/ecologia-gas-model",
    filename="gas_features.joblib",
    token="YOUR_HF_TOKEN"  # Optional if public
)

# Load model and features
model = joblib.load(model_path)
feature_columns = joblib.load(features_path)
```

### Prediction Example
```python
import pandas as pd
import numpy as np

# Prepare input data (example)
input_data = pd.DataFrame({
    'building_type': ['Office'],
    'area_sqm': [1000],
    'year_built': [2020],
    'temperature': [20.5],
    'humidity': [65],
    'hour': [14],
    'day_of_week': [1],
    'month': [6],
    # ... other required features
})

# Ensure all features are present
for col in feature_columns:
    if col not in input_data.columns:
        input_data[col] = 0

# Select features in correct order
input_data = input_data[feature_columns]

# Make prediction
prediction = model.predict(input_data)
print(f"Predicted gas_consumption (m³): {prediction[0]:.2f}")
```

## Model Limitations

- Model performance may vary based on building characteristics and regional differences
- Training data is primarily from North American buildings
- Predictions are estimates and should be validated with actual consumption data
- Model requires all input features to be provided

## Ethical Considerations

- Model is designed to help reduce energy consumption and carbon footprint
- No personal or sensitive data is used in training
- Model predictions should be used responsibly for sustainability purposes

## Citation

If you use this model, please cite:

```bibtex
@software{ecologia_energy_model,
  title = {Ecologia Gas Consumption Model},
  author = {Ecologia Energy Team},
  year = {2024},
  url = {https://huggingface.co/codealchemist01/ecologia-gas-model},
  note = {Trained on Building Data Genome Project 2 dataset}
}
```

## License

This model is released under the MIT License.

## Contact

For questions or issues, please open an issue on the repository or contact the Ecologia Energy team.

## Acknowledgments

- Building Data Genome Project 2 dataset creators
- scikit-learn and XGBoost communities
- HuggingFace for model hosting

---
*This model is part of the Ecologia sustainability platform for energy consumption prediction and carbon footprint calculation.*