Deep Learning and Optimization Approaches in Energy Management in Smart Grids Using IoT and Price-Based Demand Response with a Hybrid FHO-RERNN Approach: A Review

Abstract

The rapid evolution of smart grid technologies has transformed conventional power systems into intelligent, adaptive, and decentralized networks capable of meeting modern energy demands. Energy management plays a crucial role in ensuring efficiency, reliability, and sustainability within these systems. The integration of Internet of Things (IoT) technologies enables real-time monitoring, communication, and control of distributed energy resources, but also introduces challenges due to the variability of renewable energy sources, dynamic load demands, and fluctuating electricity prices.

To address these complexities, this review explores the application of deep learning and hybrid optimization techniques in smart grid energy management, with a focus on price-based demand response strategies. Deep learning models such as recurrent neural networks and convolutional neural networks are highlighted for their ability to extract insights from large-scale, heterogeneous data.

A key contribution is the analysis of a hybrid Fire Hawk Optimization–Recurrent Elman Neural Network (FHO-RERNN) framework, which enhances prediction accuracy and optimization efficiency for load forecasting and appliance scheduling. The paper also reviews datasets, applications, and performance metrics while discussing challenges like data privacy, cybersecurity, and computational overhead. Overall, it provides insights into developing scalable, intelligent energy management systems.