https://github.com/aditya-ranjan1234/bms-dataverse

Implementation of multiple ML models and suggestions using LLMs for energy efficiency predictions in buildings.
https://github.com/aditya-ranjan1234/bms-dataverse

building-energy datathon energy-efficiency machine-learning sdg7

Last synced: 7 months ago
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Implementation of multiple ML models and suggestions using LLMs for energy efficiency predictions in buildings.

• Host: GitHub
• URL: https://github.com/aditya-ranjan1234/bms-dataverse
• Owner: Aditya-Ranjan1234
• License: gpl-3.0
• Created: 2024-12-06T14:00:16.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-12-06T15:37:07.000Z (11 months ago)
• Last Synced: 2025-01-22T20:14:26.893Z (9 months ago)
• Topics: building-energy, datathon, energy-efficiency, machine-learning, sdg7
• Language: Jupyter Notebook
• Homepage:
• Size: 6.41 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Dataverse: Energy Efficiency Analysis  

## Project Overview  

Dataverse is an 8-hour datathon organised by BMSCE, Bangalore where participants analyze data from energy audits to identify inefficiencies and suggest improvements. Our team aimed to build a machine learning model that predicts the Energy Efficiency Rating of buildings and provide actionable recommendations for reducing energy wastage. This aligns with SDG-7: Clean and Affordable Energy.  

### Key Goals  

- **Predict Energy Efficiency:** Use machine learning models to classify buildings into energy efficiency rating categories (A, B, C, D).  

- **Identify Inefficiencies:** Highlight buildings with high energy consumption or inefficiencies.  

- **Generate Insights:** Provide actionable recommendations for improving energy efficiency, such as reducing peak hour consumption or improving insulation.  

## Team Members  

- **Aditya Ranjan**  

- **Gnanendra Naidu N**  

## Tools & Techniques  

1. **Data Preprocessing:** Cleaning, handling missing values, normalization, and feature engineering.  

2. **Machine Learning Models:**  

   - Best Results:  

     - K-Nearest Neighbors  

     - Linear Discriminant Analysis  

     - Ridge Classifier  

     - XGBoost  

   - Suggestion Models:  

     - Qwen 32B  

     - GPT-4.0  

3. **Evaluation Metric:** F1-Score to balance precision and recall across energy efficiency ratings.  

## Results  

- **Best Results Models:**  

  1. K-Nearest Neighbors  

  2. Linear Discriminant Analysis  

  3. Ridge Classifier  

  4. XGBoost  

- **Position:** Our team secured **Third Place** in the competition.  

## Key Insights  

- **Correlation Analysis:**  

  Explored relationships between energy consumption, renewable usage, peak hours, floor area, and occupants.  

- **Actionable Recommendations:**  

  - Reduce peak hour consumption.  

  - Improve insulation for buildings with high energy inefficiency.  

  - Increase renewable energy utilization.  

## Files in the Repository  

1. **BMS_Datathon_Dataverse.ipynb:** Implementation of multiple ML models and suggestions using LLMs.  

2. **Analysis_Correlation.ipynb:** Detailed analysis of correlations between features.  

3. **Dataverse.ipynb:** Refinement of models by dropping less impactful parameters like floor area.  

## License  

This project is licensed under the GNU General Public License v3.0.