{"id":24543622,"url":"https://github.com/aditya-ranjan1234/bms-dataverse","last_synced_at":"2025-03-16T07:44:47.695Z","repository":{"id":266863570,"uuid":"899552546","full_name":"Aditya-Ranjan1234/BMS-DataVerse","owner":"Aditya-Ranjan1234","description":"Implementation of multiple ML models and suggestions using LLMs for energy efficiency predictions in buildings.","archived":false,"fork":false,"pushed_at":"2024-12-06T15:37:07.000Z","size":6722,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-22T20:14:26.893Z","etag":null,"topics":["building-energy","datathon","energy-efficiency","machine-learning","sdg7"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Aditya-Ranjan1234.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-12-06T14:00:16.000Z","updated_at":"2024-12-22T05:21:22.000Z","dependencies_parsed_at":"2024-12-06T16:31:44.780Z","dependency_job_id":"ca8a5b46-9ed4-4599-ae2b-afd103408d81","html_url":"https://github.com/Aditya-Ranjan1234/BMS-DataVerse","commit_stats":null,"previous_names":["aditya-ranjan1234/bms-dataverse"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Aditya-Ranjan1234%2FBMS-DataVerse","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Aditya-Ranjan1234%2FBMS-DataVerse/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Aditya-Ranjan1234%2FBMS-DataVerse/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Aditya-Ranjan1234%2FBMS-DataVerse/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Aditya-Ranjan1234","download_url":"https://codeload.github.com/Aditya-Ranjan1234/BMS-DataVerse/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243841192,"owners_count":20356441,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["building-energy","datathon","energy-efficiency","machine-learning","sdg7"],"created_at":"2025-01-22T20:14:33.609Z","updated_at":"2025-03-16T07:44:47.661Z","avatar_url":"https://github.com/Aditya-Ranjan1234.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Dataverse: Energy Efficiency Analysis  \n\n## Project Overview  \nDataverse 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.  \n\n### Key Goals  \n- **Predict Energy Efficiency:** Use machine learning models to classify buildings into energy efficiency rating categories (A, B, C, D).  \n- **Identify Inefficiencies:** Highlight buildings with high energy consumption or inefficiencies.  \n- **Generate Insights:** Provide actionable recommendations for improving energy efficiency, such as reducing peak hour consumption or improving insulation.  \n\n## Team Members  \n- **Aditya Ranjan**  \n- **Gnanendra Naidu N**  \n\n## Tools \u0026 Techniques  \n1. **Data Preprocessing:** Cleaning, handling missing values, normalization, and feature engineering.  \n2. **Machine Learning Models:**  \n   - Best Results:  \n     - K-Nearest Neighbors  \n     - Linear Discriminant Analysis  \n     - Ridge Classifier  \n     - XGBoost  \n   - Suggestion Models:  \n     - Qwen 32B  \n     - GPT-4.0  \n\n3. **Evaluation Metric:** F1-Score to balance precision and recall across energy efficiency ratings.  \n\n## Results  \n- **Best Results Models:**  \n  1. K-Nearest Neighbors  \n  2. Linear Discriminant Analysis  \n  3. Ridge Classifier  \n  4. XGBoost  \n\n- **Position:** Our team secured **Third Place** in the competition.  \n\n## Key Insights  \n- **Correlation Analysis:**  \n  Explored relationships between energy consumption, renewable usage, peak hours, floor area, and occupants.  \n- **Actionable Recommendations:**  \n  - Reduce peak hour consumption.  \n  - Improve insulation for buildings with high energy inefficiency.  \n  - Increase renewable energy utilization.  \n\n## Files in the Repository  \n1. **BMS_Datathon_Dataverse.ipynb:** Implementation of multiple ML models and suggestions using LLMs.  \n2. **Analysis_Correlation.ipynb:** Detailed analysis of correlations between features.  \n3. **Dataverse.ipynb:** Refinement of models by dropping less impactful parameters like floor area.  \n\n## License  \nThis project is licensed under the GNU General Public License v3.0.   \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faditya-ranjan1234%2Fbms-dataverse","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faditya-ranjan1234%2Fbms-dataverse","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faditya-ranjan1234%2Fbms-dataverse/lists"}