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

Yung-Yao Chen,Chia-Ching Kung,Yu-Hsiu Lin,I-Hsuan Yen,Ming-Han Chung

doi:10.3390/s19092047

Abstract

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.

Highlights

There is a growing interest in applying recent breakthrough technologies in relevant fields such as smart homes in smart cities
For feature extraction processed through time-domain analysis, electrical features, RMSPower and peakPower, were extracted from electrical appliances monitored by the presented prototype
To meet continuously increasing electrical energy demands requested from downstream sectors of an smart grid (SG), energy management system (EMS) monitor and manage industrial, commercial, and residential electrical appliances efficiently in response to demand response (DR) signals in demand-side management (DSM)

Summary

Introduction

There is a growing interest in applying recent breakthrough technologies in relevant fields such as smart homes in smart cities. The worldwide adoption of EMSs that identify and communicate electrical energy consumption data with an intrusive deployment of smart plugs [3,4,7,8] gives rise to new user-centric IoT applications, electrical energy efficiency services, in DSM. It is based on a centralized cloud architectural model [9,10]; in smart homes connected with a smart city in an SG, deployed IoT end devices generate substantial amounts of data that must be transmitted, stored, and processed in powerful cloud computing. Compared with cloud analytics, where data gathered by IoT end devices and transmitted to cloud storage are treated in centralized cloud-centered data science analytics, edge analytics (fog computing), a promising technique dedicated and used to analyze data that need to be processed for immediately analytical, interpretable, and real-time actionable data insights at

Methods

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Discussion

Conclusion