A Home Appliance Recognition System Using the Approach of Measuring Power Consumption and Power Factor on the Electrical Panel, Based on Energy Meter ICs

Moro, Jefferson Zortéa; Duarte, Luís Fernando Caparroz; Ferreira, Elnatan Chagas; Dias, José Antonio Siqueira

doi:10.4236/cs.2013.43033

Cited by 21 publications

(20 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on. SVM based analytics have been reported for appliance type recognition from AMI data [32,33] and electricity theft detection [34,35].…”

Section: B Tools For Smart Meteringmentioning

confidence: 99%

Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey

Alahakoon

2016

IEEE Trans. Ind. Inf.

461

170

View full text Add to dashboard Cite

Smart meters have been deployed in many countries across the world since early 2000s. The smart meter as a key element for the smart grid is expected to provide economic, social and environmental benefits for multiple stakeholders. There has been much debate over the real values of smart meters. One of the key factors that will determine the success of smart meters is smart meter data analytics which deal with data acquisition, transmission, processing, and interpretation that bring benefits to all stakeholders. This paper presents a comprehensive survey of smart electricity meters and their utilization focusing on key aspects of metering process, the different stakeholder interests and technologies used to satisfy stakeholder interests. Furthermore the paper highlights the challenges as well as opportunities arising due to the advent of big data and the increasing popularity of the cloud environments.time pricing schemes, and demand-response for energy saving and efficient use of energy generated.The research and development in smart meters and their applications have been progressed rapidly in recent years and many methods and techniques have been developed. The technological scope relating to smart meters covers a diverse range of ICT technologies, such as electronics and communication, and there have already been several surveys done, e.g.[7] looks into the different applications of smart grids focusing on the communication needs and also on communication requirements of smart meters for integration into power grids.[8] is a comprehensive survey of communication technologies for smart meters which could be used to satisfy the identified needs.[9] introduces a novel smart meter communication technology, [10] examines the web and data service aspect of smart meter networks, and [11] proposes a framework for smart meter privacy. In this paper, we examine these developments from a holistic data analytic viewpoint. We will also outline potential future applications and challenges that lie ahead. As a foundation for our holistic approach the key components of electricity meter intelligence is shown as in Figure 1. The three key components capture the aspects of data, technology and stakeholders. These aspects and the role they play in smart meter intelligence is discussed in section 2.

show abstract

Section: B Tools For Smart Meteringmentioning

confidence: 99%

Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey

Alahakoon

2016

IEEE Trans. Ind. Inf.

461

170

View full text Add to dashboard Cite

show abstract

“…In order to solve the problem of overlapping signatures in the power-based method, many researchers have proposed the use of various appliance features in addition to the steady-state power draw. Several studies, for example [15][16][17], explored the use of power factors to distinguish different appliances with similar power usages. Another solution, described in [18], suggests the use of power factor, root mean square current, and voltage, as well as the phase difference.…”

Section: Nilm By Using Harmonic Content Of the Current Signalmentioning

confidence: 99%

Nonintrusive identification of residential appliances using harmonic analysis

Djordjević¹,

Simić²

2018

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

The role of nonintrusive load monitoring (NILM) is to identify the operating schedules of individual appliances and their power consumption from single-point electrical measurements. This paper discusses appliance load monitoring based on the harmonic analysis of the steady-state current, which is specifically suited for identification of nonlinear appliances. The existing harmonic-based NILM methods have limited applicability due to the fact that their complexity increases exponentially with the number of target appliances. In order to overcome this problem, this study suggests the use of the step changes of current harmonic phasors as a feature for appliance detection. The key benefit of the proposed method is that only individual appliance signatures are needed to identify load activities, as opposed to the previous NILM methods that require load signatures with respect to all possible appliance combinations.

show abstract

“…This system also makes uses Bayesian network in order to take in account the user behavior in the recognition process. Another NILM system based on measurement at circuit level was proposed in [27]. The hardware of this system is composed basically of energy meter ICs that are responsible to acquire the electrical information from the circuits and a microcontroller used to send the collected data to the clouds.…”

Section: Non-intrusive Load Monitoringmentioning

confidence: 99%

“…The researchers diverge regarding which parameters are the best for load disaggregation. Most use the active power and current for defining load signatures, some use reactive power [13,14,22,23,29] other use power factor [27] and harmonic components in the current signal [14,15,19,[22][23][24][25]30].…”

Section: Electrical Parameters Used To Define Load Signatures In Nilmmentioning

confidence: 99%