A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium's Niagara Framework for Residential Demand-Side Management.

Yung-Yao Chen,Che-Ming Chang,Fu-Sheng Chang,Ming-Hung Chen,Yu-Hsiu Lin

doi:10.3390/s21082883

Abstract

Electricity is a vital resource for various human activities, supporting customers’ lifestyles in today’s modern technologically driven society. Effective demand-side management (DSM) can alleviate ever-increasing electricity demands that arise from customers in downstream sectors of a smart grid. Compared with the traditional means of energy management systems, non-intrusive appliance load monitoring (NIALM) monitors relevant electrical appliances in a non-intrusive manner. Fog (edge) computing addresses the need to capture, process and analyze data generated and gathered by Internet of Things (IoT) end devices, and is an advanced IoT paradigm for applications in which resources, such as computing capability, of a central data center acted as cloud computing are placed at the edge of the network. The literature leaves NIALM developed over fog-cloud computing and conducted as part of a home energy management system (HEMS). In this study, a Smart HEMS prototype based on Tridium’s Niagara Framework® has been established over fog (edge)-cloud computing, where NIALM as an IoT application in energy management has also been investigated in the framework. The SHEMS prototype established over fog-cloud computing in this study utilizes an artificial neural network-based NIALM approach to non-intrusively monitor relevant electrical appliances without an intrusive deployment of plug-load power meters (smart plugs), where a two-stage NIALM approach is completed. The core entity of the SHEMS prototype is based on a compact, cognitive, embedded IoT controller that connects IoT end devices, such as sensors and meters, and serves as a gateway in a smart house/smart building for residential DSM. As demonstrated and reported in this study, the established SHEMS prototype using the investigated two-stage NIALM approach is feasible and usable.

Highlights

Introduction distributed under the terms andRecent distinct disciplines and breakthrough technologies, such as the Internet of Things (IoT), artificial intelligence (AI), big data analytics, and fog-cloud computing, are trending upwards in today’s modern technologically driven society
For non-intrusive appliance load monitoring (NIALM), this study develops AI that is trained in the cloud for off-line learning and is deployed via the Internet on the networked, embedded JACE® controller to support edge computing for on-line load monitoring
AI that operates via a DL approach based on a feed-forward, multi-layer artificial neural networks (ANNs) trained through backpropagation here is used as a load recognizer in the two-stage NIALM, where the AI-based load recognizer decomposes the acquired total energy consumption data based on learned NIALM feature data

Summary

Introduction

Recent distinct disciplines and breakthrough technologies, such as the Internet of Things (IoT), artificial intelligence (AI), big data analytics, and fog (edge)-cloud computing, are trending upwards in today’s modern technologically driven society. These technologies are increasingly becoming fundamental constituents of cities, such as in smart houses, conditions of the Creative Commons. The “smart grid” paradigm copes with ever-increasing electricity demands by customers from downstream sectors of a grid, i.e., residential, commercial, and industrial sectors, to name but a few. Smart meter data analytics holds tremendous potential for power utilities to (1) understand the power demands of their customers and (2) manage, plan, and optimize their power grid to be efficient and smart.

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