AIChatbotResearch

GridGenius

asking better questions of the grid

AI-Powered Energy Forecasting & Optimization for Bangladesh.

GridGenius project preview

What is GridGenius?

GridGenius was born out of a deep frustration with the persistent load shedding issues in Bangladesh. Growing up, I experienced countless power outages that disrupted daily life, and when I started analyzing the data, I was shocked to see how inefficient energy generation was in the country. Determined to make a difference, I took matters into my own hands and created GridGenius, an AI-powered platform designed to address energy challenges in Bangladesh by forecasting and optimizing electricity demand. Using machine learning and a Retrieval-Augmented Generation (RAG) chatbot, it provides actionable insights for energy planning. The system forecasts daily electricity demand with ML models trained on over 1800 BPDB reports, offers real-time explanations via the "Ask Genius" chatbot, and visualizes trends through an interactive dashboard.

Power crises and load shedding are persistent issues in Bangladesh due to poor demand estimation. GridGenius bridges this gap by leveraging AI to predict demand, optimize generation, and provide smart insights into seasonal gaps and inefficiencies. With a focus on transparency and usability, it empowers utilities, policymakers, and the public to make informed energy decisions.

Key Features

  1. GridOracle

    ML-based demand forecasting with BPDB data.

  2. Ask Genius

    LLM-powered chatbot for energy insights.

  3. Smart Insights

    Analyze seasonal gaps and patterns.

  4. Interactive Dashboard

    Visualize trends with Tailwind CSS.

Perfect For

(if this sounds like you)

  • Energy Planning

    Optimize generation for utilities.

  • Policy Making

    Inform decisions with data insights.

  • Sustainability

    Reduce waste in energy systems.

  • Public Awareness

    Educate users on energy trends.

Technology & Architecture

GridGenius is built on a modular, scalable architecture. The backend uses FastAPI to serve RESTful APIs for model inference and RAG-based explainability, with data retrieval from a ChromaDB vector database. The frontend, hosted on Vercel, features an interactive dashboard built with Tailwind CSS and Vanilla JS, displaying forecasts, visualizations, and a chatbot interface powered by Groq Llama 3 with HuggingFace embeddings.

The system employs multiple ML models, including Random Forest, XGBoost, and a custom Transformer Regressor for demand forecasting. Feature engineering includes outlier removal, scaling, and derived features like Demand-Generation Gap. Extensive EDA revealed key insights, such as demand increases during hotter months and reduced demand on holidays, informing the model's accuracy and usability.

What's Next

  1. planned Real-Time Updates
  2. planned Mobile App
  3. planned Expanded Datasets
  4. planned Advanced Visualizations

Get Started

Explore GridGenius today and see how AI can transform the energy sector. Want to contribute or learn more? Check out the project on GitHub!