This project aims to tackle the challenges of high energy consumption inefficiencies by leveraging advanced AI techniques, including Local LLMs (Large Language Models) and anomaly detection algorithms like IsolationForest. The solution is designed to enhance energy efficiency, reduce costs, and provide actionable insights to users while ensuring data privacy through secure, local processing.
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Energy Usage Analysis:
- Historical data analysis to identify consumption patterns.
- Pinpointing inefficiencies and abnormal spikes in energy usage.
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Interactive Dashboards:
- Intuitive visualizations of energy consumption trends.
- Comparison of energy plans and cost analysis.
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Explainable AI:
- Explainable predictions using SHAP values for user transparency.
- Recommendations tailored to improve household energy efficiency.
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Privacy-Preserving AI:
- Use of locally deployed LLMs to eliminate reliance on cloud services.
- Ensures user data remains secure and private.
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Anomaly Detection:
- IsolationForest model to detect irregular spikes in energy usage.
- Granular insights into devices or appliances causing inefficiencies.
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Real-Time Monitoring:
- Notifications and alerts for users on abnormal spikes.
- Preventive actions recommended based on appliance-level analysis.
- What are the inefficiencies in household energy consumption?
- How can energy usage patterns be optimized?
- How can utility companies balance energy supply and demand effectively?
- What changes can users make to reduce costs and environmental impact?
- How can data privacy be maintained while delivering actionable insights?
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Machine Learning:
- IsolationForest for anomaly detection.
- Local LLMs for data analysis and personalized insights.
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Visualization:
- Streamlit for interactive dashboards.
- Plotly for dynamic, user-friendly visualizations.
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Automation:
- Apache Airflow for workflow orchestration and data pipelines.
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Data Security:
- Local processing to maintain user data privacy.
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Data Collection and Integration:
- Aggregates energy usage data, weather data, and appliance-level metrics.
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Data Cleaning and Preprocessing:
- Handles anomalies in date ranges, missing data, and outliers.
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Interactive Dashboard:
- Provides insights into energy usage trends, predictive costs, and recommendations.
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Explainable AI Techniques:
- SHAP values to explain predictions in a user-friendly way.
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Real-Time Alerts:
- Notifications for users during energy consumption spikes with targeted solutions.
- Enhance real-time monitoring to isolate and analyze specific appliances causing energy spikes.
- Improve user alerts with detailed appliance-level insights and optimization suggestions.
- Scale predictive analytics to include broader datasets for seasonal and behavioral patterns.
- Clone this repository:
git clone https://github.com/username/energy-optimization.git - Navigate to the project directory:
cd energy-optimization - Install required dependencies:
pip install -r requirements.txt - Run the Streamlit dashboard:
streamlit run app.py
- Python 3.8+
- Libraries: Streamlit, Snowflake Snowpark, Pandas, Plotly, Scikit-learn, SHAP
- Apache Airflow (for automation workflows)
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├── app.py # Streamlit application
├── models/ # Machine learning models
├── data/ # Sample data
├── notebooks/ # Jupyter notebooks for exploratory data analysis
├── requirements.txt # Project dependencies
└── README.md # Project documentation
Contributions are welcome! Please follow the guidelines below:
- Fork the repository.
- Create a feature branch (
git checkout -b feature-name). - Commit your changes (
git commit -m "Add feature"). - Push to the branch (
git push origin feature-name). - Submit a pull request.
Special thanks to:
- The open-source community for tools like Streamlit and Plotly.
- Utility companies for providing anonymized datasets.
This project is licensed under the MIT License. See the LICENSE file for details.
For any questions, feedback, or contributions:
- Email: smitsaurabh20@gmail.com