Energy-Net: A Deep Learning Approach for Smart Energy Management in IoT-Based Smart Cities

Mohamed Abdel-Basset, Hossam Hawash, Ripon K. Chakrabortty, Michael Ryan

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


Although intelligent load forecasting is essential for optimal energy management (EM) in smart cities, there is a lack of current research exploring EM in well-regulated Internet-of-Things (IoT) networks. This article develops a new deep learning (DL) model for efficient forecasting of short-term energy consumption while maintaining effective communication between energy providers and users. The proposed Energy-Net stack comprises multiple stacked spatiotemporal modules, where each module consists of a temporal transformer (TT) submodule and a spatial transformer (ST) submodule. The TT models the temporal relationships in load data; and the ST submodule extracts hidden spatial information by integrating convolutional layers and includes an improved self-attention mechanism. The experimental evaluation on IHPEC and independent system operator New England (ISO-NE) data set demonstrates the superiority of Energy-Net over recent cutting-edge DL models with root mean-square error (RMSE) of 0.354 and 0.535, respectively. The computational complexity of Energy-Net is appropriate for dependable resource-constrained IoT devices (i.e., fog nodes or edge nodes) linked to a joint IoT-cloud server that interacts with connected smart grids to handle EM tasks.

Original languageEnglish
Article number9371013
Pages (from-to)12422-12435
Number of pages14
JournalIEEE Internet of Things Journal
Issue number15
Publication statusPublished - 1 Aug 2021
Externally publishedYes


Dive into the research topics of 'Energy-Net: A Deep Learning Approach for Smart Energy Management in IoT-Based Smart Cities'. Together they form a unique fingerprint.

Cite this