Intelligent edge analytics for load identification in smart meters

Sirojan, Tharmakulasingam; Phung, B.T.; Ambikairajah, Eliathamby

doi:10.1109/isgt-asia.2017.8378414

Cited by 19 publications

(10 citation statements)

References 9 publications

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“…Chang et al designed a load identification system based on the characteristics of residential electricity consumption and made it run in an Intel Atom processor [ 16 ]. Sirojan et al implemented an embedded neural network for NILM by using the National Instruments (NI) myRIO-1900 platform containing a field programmable gate array (FPGA) and an ARM Cortex-A9 processor [ 17 ]. However, the chip running NILM algorithm in the above approach is still extremely costly compared to the embedded microcontroller.…”

Section: Related Workmentioning

confidence: 99%

DeepEdge: A Novel Appliance Identification Edge Platform for Data Gathering, Capturing and Labeling

Wang

Zhang

et al. 2022

Sensors

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With the development of the Internet of Things for smart grid, the requirement for appliance monitoring has become an important topic. The first and most important step in appliance monitoring is to identify the type of appliance. Most of the existing appliance identification platforms are cloud based, thus they consume large computing resources and memory. Therefore, it is necessary to explore an edge identification platform with a low cost. In this work, a novel appliance identification edge platform for data gathering, capturing and labeling is proposed. Experiments show that this platform can achieve an average appliance identification accuracy of 98.5% and improve the accuracy of non-intrusive load disaggregation algorithms.

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Section: Related Workmentioning

confidence: 99%

DeepEdge: A Novel Appliance Identification Edge Platform for Data Gathering, Capturing and Labeling

Wang

Zhang

et al. 2022

Sensors

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“…An intelligent edge analytics approach for load identification in smart meters has been studied in [12]. Sirojan et al have designed an embedded edge computing paradigm for real-time smart meter data analytics in [13].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Federated Learning for GMEC With Time-Varying Wireless Link

et al. 2021

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Smart grid applications, such as predicting energy consumption, grid user behavior analysis and predicting energy theft, etc., are data-driven applications that require machine learning with a wealth of data generated from Internet of Things (IoT) based metering devices. However, traditional methods of uploading this huge data to the remote cloud for data analytics may be low efficient due to the non-negligible network transmission delay. By deploying a number of computing-enabled devices at the network edge, edge computing supports the implementation of machine learning close to the power grid environment. Considering the limited computing resources of edge devices and non-independent and identical (non-IID) data source, federated learning is a feasible edge computing based machine learning model. In federated learning, distributed mobile clients and a federated server collaborate to perform machine learning. Generally, the more clients to join the federated learning, the faster to obtain learning convergence and the higher resource utility. However, the communications between clients and the server in training rounds of federated learning may fail due to time-varying link reliability properties in a wireless network of smart grid, which not only slows down the model convergence rate but also wastes resources, such as energy consumption for invalid local training. This paper studies a dynamic federated learning problem in a power grid mobile edge computing (GMEC) environment, considering the high dynamic of link reliability. We design a delay deadline constrained federated learning framework to avoid extremely long training delay, and then formulate a dynamic client selection problem for computing utility maximization in such learning framework. Two online client selection algorithms, including climax greedy and uti-positive guarantee, are proposed to address the problem. The theoretical analysis and simulation results are conducted to illustrate the efficiency of the proposal. INDEX TERMS Mobile edge computing, machine learning, federated learning, smart grid, link reliability.

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“…The framework is secure and reliable. Sirojan et al [82] proposed an intelligent Edge analytics technique for load identification in the smart meters. The method improves the Non-Intrusive Load Monitoring (NILM) of the metering unit in the prime circuit panel.…”

Section: Smart Technology and Edge Analyticsmentioning

confidence: 99%

A Review on Edge Analytics: Issues, Challenges, Opportunities, Promises, Future Directions, and Applications

Nayak¹,

Patgiri²,

Waikhom³

et al. 2021

Preprint

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Edge technology aims to bring Cloud resources (specifically, the compute, storage, and network) to the closed proximity of the Edge devices, i.e., smart devices where the data are produced and consumed. Embedding computing and application in Edge devices lead to emerging of two new concepts in Edge technology, namely, Edge computing and Edge analytics. Edge analytics uses some techniques or algorithms to analyze the data generated by the Edge devices. With the emerging of Edge analytics, the Edge devices have become a complete set. Currently, Edge analytics is unable to provide full support for the execution of the analytic techniques. The Edge devices cannot execute advanced and sophisticated analytic algorithms following various constraints such as limited power supply, small memory size, limited resources, etc. This article aims to provide a detailed discussion on Edge analytics. A clear explanation to distinguish between the three concepts of Edge technology, namely, Edge devices, Edge computing, and Edge analytics, along with their issues. Furthermore, the article discusses the implementation of Edge analytics to solve many problems in various areas such as retail, agriculture, industry, and healthcare. In addition, the research papers of the state-of-the-art edge analytics are rigorously reviewed in this article to explore the existing issues, emerging challenges, research opportunities and their directions, and applications.

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Intelligent edge analytics for load identification in smart meters

Cited by 19 publications

References 9 publications

DeepEdge: A Novel Appliance Identification Edge Platform for Data Gathering, Capturing and Labeling

DeepEdge: A Novel Appliance Identification Edge Platform for Data Gathering, Capturing and Labeling

Dynamic Federated Learning for GMEC With Time-Varying Wireless Link

A Review on Edge Analytics: Issues, Challenges, Opportunities, Promises, Future Directions, and Applications

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