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Open AccessArticle

Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes

1
Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei 106, Taiwan
2
Department of Electrical Engineering, Allied AI Biomedical Research Center, Southern Taiwan University of Science and Technology, Tainan 71005, Taiwan
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 2047; https://doi.org/10.3390/s19092047
Received: 6 March 2019 / Revised: 27 April 2019 / Accepted: 29 April 2019 / Published: 2 May 2019
(This article belongs to the Special Issue Selected Papers from INNOV 2018)
In a smart home linked to a smart grid (SG), demand-side management (DSM) has the potential to reduce electricity costs and carbon/chlorofluorocarbon emissions, which are associated with electricity used in today’s modern society. To meet continuously increasing electrical energy demands requested from downstream sectors in an SG, energy management systems (EMS), developed with paradigms of artificial intelligence (AI) across Internet of things (IoT) and conducted in fields of interest, monitor, manage, and analyze industrial, commercial, and residential electrical appliances efficiently in response to demand response (DR) signals as DSM. Usually, a DSM service provided by utilities for consumers in an SG is based on cloud-centered data science analytics. However, such cloud-centered data science analytics service involved for DSM is mostly far away from on-site IoT end devices, such as DR switches/power meters/smart meters, which is usually unacceptable for latency-sensitive user-centric IoT applications in DSM. This implies that, for instance, IoT end devices deployed on-site for latency-sensitive user-centric IoT applications in DSM should be aware of immediately analytical, interpretable, and real-time actionable data insights processed on and identified by IoT end devices at IoT sources. Therefore, this work designs and implements a smart edge analytics-empowered power meter prototype considering advanced AI in DSM for smart homes. The prototype in this work works in a cloud analytics-assisted electrical EMS architecture, which is designed and implemented as edge analytics in the architecture described and developed toward a next-generation smart sensing infrastructure for smart homes. Two different types of AI deployed on-site on the prototype are conducted for DSM and compared in this work. The experimentation reported in this work shows the architecture described with the prototype in this work is feasible and workable. View Full-Text
Keywords: artificial intelligence; cloud analytics; demand-side management; edge/fog analytics; electrical energy management; Internet of things; smart grid; smart homes/factories; smart sensing artificial intelligence; cloud analytics; demand-side management; edge/fog analytics; electrical energy management; Internet of things; smart grid; smart homes/factories; smart sensing
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MDPI and ACS Style

Chen, Y.-Y.; Lin, Y.-H.; Kung, C.-C.; Chung, M.-H.; Yen, I-H. Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes. Sensors 2019, 19, 2047. https://doi.org/10.3390/s19092047

AMA Style

Chen Y-Y, Lin Y-H, Kung C-C, Chung M-H, Yen I-H. Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes. Sensors. 2019; 19(9):2047. https://doi.org/10.3390/s19092047

Chicago/Turabian Style

Chen, Yung-Yao; Lin, Yu-Hsiu; Kung, Chia-Ching; Chung, Ming-Han; Yen, I-Hsuan. 2019. "Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes" Sensors 19, no. 9: 2047. https://doi.org/10.3390/s19092047

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