The AI-powered Smart Energy Management System addresses high energy costs and unreliable electricity in rural telecom networks. Using datasets like VIIRS Nighttime Lights, OpenCellID, Sentinel-2, and Africa Bandwidth Maps, the system optimizes energy consumption across network nodes. It employs machine learning to analyze real-time energy usage and predict consumption patterns, enabling dynamic power-saving modes for off-peak hours. Additionally, renewable energy integration is facilitated by identifying optimal locations for solar-powered Base Transceiver Stations (BTS) using Sentinel-2 imagery. The system features a real-time AI-powered dashboard, offering network operators insights into energy consumption, grid stability, and alternative energy sources. OpenStreetMap and Global Electricity Grid data help in planning infrastructure placement. This solution not only reduces operational costs but also contributes to sustainability goals by enabling efficient power utilization. Its scalability extends to multiple rural telecom sites, making it a vital innovation in network design and strategic planning.

This platform simplifies data analysis by allowing users to interact with datasets using natural language. Built with Streamlit, it provides an intuitive interface where users can easily upload CSV files and ask questions to get instant insights without needing any programming skills. The tech stack behind the platform includes Pandas for data manipulation and Natural Language Processing (NLP) models to interpret user queries. Streamlit’s reactive UI ensures fast and smooth interactions, while the uploaded datasets are stored and processed efficiently, providing quick analysis and results on-demand.