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Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings
Mahmoud Elsisi1,2, Minh-Quang Tran1,3, Karar Mahmoud4,5
1Industry 4.0 Implementation Center, Center for Cyber-Physical System Innovation, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
Sensors (Basel, Switzerland)
|February 6, 2021
Summary
This study introduces a deep learning and Internet of Things (IoT) approach to reduce energy consumption by intelligently controlling air conditioners. The system uses AI to detect people, optimizing cooling based on occupancy for significant energy savings.
Area of Science:
- Artificial Intelligence
- Internet of Things
- Sustainable Energy
Background:
- Energy consumption is a major global challenge, particularly in industrial and domestic sectors.
- The Internet of Things (IoT) is pivotal for Industry 4.0, enabling device connectivity and AI-driven control.
- Optimizing energy usage in buildings requires intelligent management of appliances like air conditioners.
Purpose of the Study:
- To develop and evaluate a novel deep learning and IoT-based system for intelligent air conditioner control.
- To reduce energy consumption in smart buildings through AI-powered occupancy detection and HVAC management.
- To enhance decision-making processes for energy consumption via real-time data monitoring.
Main Methods:
- Implemented a deep learning-based people detection system using the YOLOv3 algorithm to count individuals in specific areas.
- Integrated the people detection system with an IoT platform to monitor occupancy and air conditioner status.
- Simulated intensive test scenarios in a smart building environment to validate the system's effectiveness.
Main Results:
- The deep learning algorithm accurately detected the number of persons in the designated area, demonstrating proficiency in modeling complex data relationships.
- The system successfully published real-time occupancy and air conditioner status data to an IoT platform dashboard.
- Simulation results confirmed the system's efficacy in optimizing air conditioner operation for energy reduction.
Conclusions:
- The proposed deep learning and IoT approach effectively reduces energy consumption by intelligently managing air conditioners based on real-time occupancy.
- The system provides accurate people detection and enables remote management of controllable devices, showcasing its versatility.
- This approach offers a promising solution for enhancing energy efficiency and smart building management.
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