ZigBee wireless smart plug network with RSSI multi‐lateration‐based proximity estimation and parallelised machine learning capabilities for demand response. (1st December 2020)

• Record Type: Journal Article
• Authors: Deese, Anthony S.; Daum, Julian
• Is Part Of: IET wireless sensor systems. Volume 10:Number 6(2020)
• Page Start: 283
• Page End: 291
• Publication Date: 2020-12-01
• Subjects: wireless sensor networks -- indoor communication -- power engineering computing -- optimisation -- Zigbee -- learning (artificial intelligence) -- pattern clustering -- sensor fusion -- neural nets; parallelised neural network training method -- load automation -- multiple networks -- partitioned neural networks -- residential space -- commercial space -- innovations complement -- ZigBee wireless smart plug network -- RSSI multilateration‐based proximity estimation -- parallelised machine learning capabilities -- ZigBee technology -- electric loads -- residential spaces -- commercial spaces -- demand response initiative -- custom smart plug -- wireless communication -- electric load actuation capabilities -- innovative upgrades -- commercially available smart plugs -- multiple sensors -- robust system‐wide optimisation -- innovative smart plug eco‐system improvements -- received signal strength indicator -- relative proximities -- RSSI values -- ZigBee network -- authors -- RSSI observation -- innovation; Wireless sensor networks -- Periodicals; 681.2
• DOI: 10.1049/iet-wss.2018.5047
• Languages: English
• ISSNs: 2043-6386

Abstract

Abstract : This study explores how wireless ZigBee technology may be applied to automation of electric loads in residential and commercial spaces, allowing to participate in demand response initiatives. The authors discuss development of a custom smart plug with sensing, wireless communication, and electric load actuation capabilities along with several innovative upgrades. There are many commercially available smart plugs that contain multiple sensors and relays. However, very few provide the ability to effectively estimate the proximity between modules or the ability to perform robust system‐wide optimisation. The authors propose two innovative smart plug eco‐system improvements. One is the use of a received signal strength indicator (RSSI) multi‐lateration‐based method to estimate the relative proximities of modules. The RSSI values for almost all transmission paths within the ZigBee network are acquired via the authors' forced network reconfiguration algorithm, addressing the limitations of RSSI observation within a star structure. A second innovation is the development of a parallelised neural network training method for application to load automation. The authors use a k ‐means clustering algorithm to divide training data into subsets such that training may be parallelised.