Deep Learning-Based Industry 4.0 and Internet of Things Towards Effective Energy Management for Smart Buildings

Mahmoud Elsisi, Minh-Quang Tran, Karar Mahmoud, Matti Lehtonen, Mohamed M. F. Darwish

Research output: Contribution to journalArticleScientificpeer-review

72 Citations (Scopus)
101 Downloads (Pure)


Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper’s innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices.
Original languageEnglish
Article number1038
Pages (from-to)1-19
Number of pages19
Issue number4
Publication statusPublished - 2 Feb 2021
MoE publication typeA1 Journal article-refereed


  • Smart systems
  • Internet of Things
  • Machine learning
  • Energy management


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