Real-Time Energy Demand Forecasting and Adaptive Demand Response Optimization for IoT-Enabled Smart Grids

The evolution of energy systems concerning IoT-enabled smart grids require new innovative solutions to address enormous open issues in demand-supply balance, grid reliability, and sustainability. In this research work, attention is centered on integrating real-time energy demand forecast and adaptive demand response optimization. This is solely to improve efficiency and resilience of modern smart grids. We use Advanced ML technique known as Long Short-Term Memory (LSTM) networks to determine accurate energy demand forecast by capturing temporal dependencies and non-linear trends when consuming energy data. Using Simulation, we present model’s efficacy in achieving accurate forecast using Mean Absolute Percentage Error (MAPE) of 5.6%, a peak load reduction of 20%, and energy cost savings that exceeds 24%. We validate Computational efficiency with execution times that is better for real-time operation and grid scalability of 10,000 IoT devices. these results pave way for future research in hybrid forecast analysis, and multi-objective optimization. This can ensure stability of the grid in dynamic and decentralized energy landscape