Hybrid Edge-Cloud Approach for Renewable Energy Management Using Deep Learning with Predictive Analytics

The integration of renewable energy sources into smart grids requires advanced management systems to optimize energy production, storage, and distribution. Conventional methods like Static Energy Allocation (SEA) and Rule-Based Control (RBC) struggle with real-time adaptability, leading to inefficiencies in handling the variable nature of renewable energy. SEA and RBC lack the predictive capabilities needed to effectively manage dynamic energy generation, resulting in increased energy losses and operational costs.This paper proposes a Hybrid Edge-Cloud Renewable Energy Management System (HECREMS) using Deep Learning with Predictive Analytics (DLPA). HECREMS integrates edge computing for real-time data processing with cloud computing for enhanced predictive analytics, providing a robust framework for efficient energy management. The proposed system leverages the low latency of edge devices and the computational power of the cloud to improve energy forecasting and decision-making. HECREMS outperforms traditional SEA and RBC methods, achieving a $0.25 \%$ improvement in energy utilization efficiency, a $\mathbf{0. 2 0 \%}$ reduction in operational costs, and a $0.30 \%$ increase in system responsiveness. By addressing the drawbacks of conventional approaches, HECREMS demonstrates significant advancements in the efficient and sustainable management of renewable energy within smart grids.