Development of an adaptive device-free human detection system for residential lighting load control

•A device-free human detection system using RSSI for residential lighting load control was developed.•A device-free human detection algorithm as the adaptive algorithm was proposed.•A hardware set for lighting load control and a lighting load control method were developed.•The system could detect th...

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Vydáno v:Computers & Electrical Engineering Ročník 93; s. 107233
Hlavní autoři: Santiprapan, Phonsit, Sengchuai, Kiattisak, Jindapetch, Nattha, Saito, Hiroshi, Booranawong, Apidet
Médium: Journal Article
Jazyk:angličtina
japonština
Vydáno: Amsterdam Elsevier Ltd 01.07.2021
Elsevier BV
Témata:
Adaptive control
Algorithms
Control methods
Device-free detection
Energy consumption
Human movement
Lighting
Lighting load control
Motorcycles
Real time
RSSI
Signal strength
Walking
Wireless networks
Wireless sensor network
Wireless sensor network
RSSI
Lighting load control
Human movement
Device-free detection
ISSN:0045-7906, 1879-0755
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Shrnutí:•A device-free human detection system using RSSI for residential lighting load control was developed.•A device-free human detection algorithm as the adaptive algorithm was proposed.•A hardware set for lighting load control and a lighting load control method were developed.•The system could detect the moving persons with the accuracy of 100%.•The system could control the residential lighting load in real-time with electricity energy saving. A real-time device-free human detection system using received signal strength indicator (RSSI) for residential lighting load control is developed in this work. The proposed system consists of four parts: a designed wireless network based on ZigBee 2.4 GHz, RSSI filters using moving average (MA) and exponentially weighted moving average (EWMA) techniques, an adaptive device-free human detection algorithm, and a hardware set for lighting load control and a lighting control method. Experiments are carried out in a laboratory with two scenarios: a walking female (a home scenario) and a walking female and a man with his motorcycle (a parking scenario). Results show that our system can detect the moving persons with 100% accuracy and can control the residential lighting in real-time. By the proposed system, the cumulative electricity energy consumption of the lighting load reduces by (72.12% and 29.31%) for the first scenario and (86.19% and 54.84%) for the second scenario. [Display omitted]
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ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2021.107233