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

Authors
  • Elsisi, Mahmoud1, 2
  • Tran, Minh-Quang1, 3
  • Mahmoud, Karar4, 5
  • Lehtonen, Matti4
  • Darwish, Mohamed M F2, 4
  • 1 Industry 4.0 Implementation Center, Center for Cyber-Physical System Innovation, National Taiwan University of Science and Technology, Taipei 10607, Taiwan. , (Taiwan)
  • 2 Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt. , (Egypt)
  • 3 Department of Mechanical Engineering, Thai Nguyen University of Technology, 3/2 Street, Tich Luong Ward, Thai Nguyen 250000, Vietnam.
  • 4 Department of Electrical Engineering and Automation, Aalto University, FI-00076 Espoo, Finland. , (Finland)
  • 5 Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt. , (Egypt)
Type
Published Article
Journal
Sensors
Publisher
MDPI AG
Publication Date
Feb 03, 2021
Volume
21
Issue
4
Identifiers
DOI: 10.3390/s21041038
PMID: 33546436
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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.

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