GridGenius - AI-Powered Energy Optimization

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🚀 Project Overview

Project Title:

GridGenius – AI-Powered Energy Optimization
Energy Demand Forecasting Using Machine Learning

Brief Summary:

• Develop a machine learning model for forecasting urban energy demand using custom-extracted data.
• Optimize energy supply, minimize wastage, and enhance operational efficiency.
• Deploy the forecasting model via a web interface with an integrated LLM for intelligent insights.

Problem Statement:

• Inefficient energy management leads to wastage and higher costs.
• The inability to accurately predict demand causes supply-demand mismatches.
• The need for a scalable and interpretable ML-based forecasting solution.

Expected Outcome:

• Accurate energy demand predictions to aid utility providers.
• Reduction in operational costs and carbon footprint.
• A web-based platform providing actionable insights with an LLM-powered interface.

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📊 Dataset Details

Source:

• Custom dataset extracted from: BPDB Daily Generation Archive

Estimated Features:

Feature	Description
Date	Daily record date
Day Probable Peak	Predicted peak generation for the day
Evening Probable Peak	Predicted peak generation for the evening
Actual Demand	Real energy consumption for the day
Environmental Factors	Temperature, Humidity, Weather Conditions

Data Collection Process:

1. Automate extraction of daily reports using web scraping.
2. Preprocess data to remove inconsistencies.
3. Store structured data for model training.

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⚙️ Complete ML Pipeline

1. Data Collection: Web scraping and processing from BPDB.
2. Data Preprocessing: Handling missing values. Feature engineering (time-based trends, weather impact).
3. Exploratory Data Analysis (EDA): Visualization of seasonal trends. Correlation analysis.
4. Model Selection and Training: Evaluate traditional and advanced ML models.
5. Model Evaluation: Metrics: RMSE, MAPE, MAE.
6. Deployment: Web application for forecasting and visualization. Integration with LLM for analytical insights.

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🔍 Problem Type Definition

Category: Time Series Forecasting
Goal: Predict future energy demand based on historical consumption and environmental factors.
Evaluation Metrics:

• Root Mean Square Error (RMSE)
• Mean Absolute Percentage Error (MAPE)
• Mean Absolute Error (MAE)

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🏗️ High-Level Software Architecture

• Data Collection Layer: Scraper to extract reports from BPDB.
• Processing Layer: Preprocessing, feature extraction.
• Model Layer: ML models (LSTM, ARIMA, XGBoost, etc.).
• Web Interface Layer: Flask/Django backend with a React frontend.
• LLM Integration: Assist users with querying and insights.

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🛠️ Technology Stack

Component	Technology Choices
Data Collection	Python (BeautifulSoup, Requests)
Data Processing	Pandas, NumPy
Model Training	Scikit-learn, TensorFlow, XGBoost
Web Framework	Flask / Django
Frontend	React.js
Database	PostgreSQL / SQLite
Deployment	AWS/GCP/Azure, Docker, CI/CD pipelines

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🌟 Proposed Novelty

1. Real-Time Prediction & Visualization: Interactive web dashboard with live updates.
2. LLM-Powered Insights: Users can interact with an AI assistant to query predictions and trends.
3. Scalability: The model can be extended to other cities with minimal adaptation.

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📚 References

1. "Energy Demand Forecasting Using Machine Learning Perspective Bangladesh"

   • Avijit Paul Piyal et al., DOI: 10.1109/GlobConHT56829.2023.10087679

2. "Short-Term Electrical Load Prediction for Future Generation Using Hybrid Deep Learning Model"

   • S. M. Anowarul Haque Sonet et al., DOI: 10.1109/ICAEEE54957.2022.9836359

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👥 The Team:

This project will be developed by:

Name	Institution	ID	GitHub	Followers
Rajin Khan	North South University	2212708042
Aurongojeb Lishad	North South University	2212457042
Pranoy Saha	North South University	2131183642
Sadia Islam Mou	North South University	2131724642

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