The code and dataset used in the published paper: Smart grid stability prediction using Adaptive Aquila Optimizer and ensemble stacked BiLSTM

Federated Learning on Smart Meter Dataset for load forecasting using clustering and light weight FNN

This project builds a self-learning, hybrid IoT architecture that connects every household node and transformer to a central intelligence layer — providing real-time visibility, load balancing, and automated control across both urban and rural grids.