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

Tao Han, Khan Muhammad, Tanveer Hussain, Jaime Lloret, Sung Wook Baik

Research output: Contribution to journalArticle (journal)peer-review

80 Citations (Scopus)


Green energy management is an economical solution for better energy usage, but the employed literature lacks focusing on the potentials of edge intelligence in controllable Internet of Things (IoT). Therefore, in this article, we focus on the requirements of todays' smart grids, homes, and industries to propose a deep-learning-based framework for intelligent energy management. We predict future energy consumption for short intervals of time as well as provide an efficient way of communication between energy distributors and consumers. The key contributions include edge devices-based real-time energy management via common cloud-based data supervising server, optimal normalization technique selection, and a novel sequence learning-based energy forecasting mechanism with reduced time complexity and lowest error rates. In the proposed framework, edge devices relate to a common cloud server in an IoT network that communicates with the associated smart grids to effectively continue the energy demand and response phenomenon. We apply several preprocessing techniques to deal with the diverse nature of electricity data, followed by an efficient decision-making algorithm for short-term forecasting and implement it over resource-constrained devices. We perform extensive experiments and witness 0.15 and 3.77 units reduced mean-square error (MSE) and root MSE (RMSE) for residential and commercial datasets, respectively.
Original languageEnglish
Pages (from-to)3170-3179
JournalIEEE Internet of Things Journal
Issue number5
Publication statusPublished - 1 Mar 2021


  • Forecasting
  • Energy management
  • Smart grids
  • Load forecasting
  • Machine learning


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