An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks

Green energy management is an economical solution for better usage, but the employed literature lacks focusing on potentials of edge intelligence in controllable Internet Things (IoT). Therefore, this article, we focus requirements todays' smart grids, homes, and industries to propose a deep-learning-based framework intelligent management. We predict future consumption short intervals time as well provide efficient way communication between distributors consumers. The key contributions include devices-based real-time via common cloud-based data supervising server, optimal normalization technique selection, novel sequence learning-based forecasting mechanism with reduced complexity lowest error rates. In proposed framework, devices relate cloud server IoT network that communicates associated grids effectively continue demand response phenomenon. apply several preprocessing techniques deal diverse nature electricity data, followed by decision-making algorithm short-term implement it over resource-constrained devices. perform extensive experiments witness 0.15 3.77 units mean-square (MSE) root MSE (RMSE) residential commercial datasets, respectively.