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...

Full description

Saved in:
Bibliographic Details
Published in:Computers & electrical engineering 2021-07, Vol.93, p.107233, Article 107233
Main Authors: Santiprapan, Phonsit, Sengchuai, Kiattisak, Jindapetch, Nattha, Saito, Hiroshi, Booranawong, Apidet
Format: Article
Language:English
Subjects:
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•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]
ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2021.107233