Low-cost appliance state sensing for energy disaggregation

Wu, Tianji; Srivastava, Mani

doi:10.1145/2422531.2422542

Cited by 11 publications

(11 citation statements)

References 5 publications

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“…A possible solution could be to adopt a hybrid approach, that is, installing various types of sensors to capture environmental and contextual information can improve load disaggregation [Jaeyeong et al 2011]. Such sensors might include, for example, a plug-based sensor that reports the state of the appliance [Wu and Srivastava 2012]. One benefit that accrues from this approach is that data collected from the distributed sensors may also contribute to automating the manual training phase of NILM.…”

Section: Discussionmentioning

confidence: 99%

A Review of Wireless-Sensor-Network-Enabled Building Energy Management Systems

Kazmi

O’Grady

Delaney

et al. 2014

ACM Trans. Sen. Netw.

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Reducing energy consumption within buildings has been an active area of research in the past decade; more recently, there has been an increased influx of activity, motivated by a variety of issues including legislative, tax-related, as well as an increased awareness of energy-related issues. Energy usage both in commercial and residential buildings represents a significant portion of overall energy consumption; however, much of this may be categorized as waste, that is, energy usage that does not fulfil a definite purpose. In the past decade, the viability of Wireless Sensor Network (WSN) technologies has been demonstrated, leading to increased possibilities for novel services for building energy management. This development has resulted in numerous approaches being proposed for harnessing WSNs for energy management and conservation. This article surveys the state-of-the-art in building energy management systems. A generic architecture is proposed after which a detailed taxonomy of existing documented systems is presented. Gaps in the literature are highlighted and directions for future research identified.Additional Key Words and Phrases: Applications of sensor and actuator networks, energy management and control, modelling of systems and physical environments, sensor fusion and distributed inference, simulation tools and environments, building energy management systems, energy usage feedback and control ACM Reference Format:

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Section: Discussionmentioning

confidence: 99%

A Review of Wireless-Sensor-Network-Enabled Building Energy Management Systems

Kazmi

O’Grady

Delaney

et al. 2014

ACM Trans. Sen. Netw.

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“…This analysis explores devices that, similar to PowerBlade, meter consumption at the outlet where power is being drawn, but methods exist for acquiring this data without physical metering. Wu et al demonstrate the ability to determine appliance on/off state through deployed sensors, and they use preexisting knowledge of the draw of each of these states to determine total power [30]. Further, ElectriSense [24] is able to determine device state simply by monitoring AC voltage at a single point and measuring the EMI generated by switched mode power supplies and propagated by the wiring throughout the building.…”

Section: Alternate Power Metering Methodsmentioning

confidence: 99%

PowerBlade

DeBruin

Ghena

Kuo

et al. 2015

Proceedings of the 13th ACM Conference on Embedded Networked Sensor Systems

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We present PowerBlade, the smallest, lowest cost, and lowest power AC plug-load meter that measures real, reactive and apparent power, and reports this data, along with cumulative energy consumption, over an industry-standard Bluetooth Low Energy radio. Achieving this design point requires revisiting every aspect of conventional power meters: a new method of acquiring voltage; a non-invasive, planar method of current measurement; an efficient and accurate method of computing power from the voltage and current channels; a radio interface that leverages nearby smart phones to display data and report it to the cloud; and a retro power supply re-imagined with vastly lower current draw, allowing extreme miniaturization. PowerBlade occupies a mere 1" × 1" footprint, offers a 1 /16" profile, draws less than 180 mW itself, offers 1.13% error on unity power factor loads in the 2-1200 W range and slightly worse for non-linear and reactive loads, and costs $11 in modest quantities of about 1,000 units. This new design point enables affordable large-scale studies of plug-load energy usage-an area of growing national importance.

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“…The input for the standard and voting χ 2 GOF method is a power signal. In [8], it is mentioned that noise and spikes in the power trace can lead to false detected events. As a solution to remove the noise, a median filter is applied on the power signal.…”

Section: Preprocessing the Datamentioning

confidence: 99%

Optimized statistical test for event detection in non-intrusive load monitoring

Baets

Ruyssinck

Develder

et al. 2017

2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Syst

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Event detection plays an important role in nonintrusive load monitoring to accurately detect when appliances are switched on or off in a residential environment. Besides being accurate, it is important that these methods are robust on real-life power traces. This paper shows that some state-of-the-art event detection methods may miss events when there is a substantial base load caused by active power consuming devices. In order to address this problem, this paper extends the existing chi-squared goodness-of-fit test with a a voting scheme. Furthermore, a work flow is proposed using surrogate-based optimisation for tuning the parameters of these tests in an efficient way. Results on the BLUED dataset indicate that the novel voting chi-squared GOF method outperforms the standard chi-squared GOF test when applied to traces with a higher base load.

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Low-cost appliance state sensing for energy disaggregation

Cited by 11 publications

References 5 publications

A Review of Wireless-Sensor-Network-Enabled Building Energy Management Systems

A Review of Wireless-Sensor-Network-Enabled Building Energy Management Systems

PowerBlade

Optimized statistical test for event detection in non-intrusive load monitoring

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