Energy and IOTAn engineer's perspective

Capps, Madison

doi:10.1145/2845133

Cited by 3 publications

(2 citation statements)

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“…Due to the large-scale deployment of smart meters by utilities, there has been a resurgence in interest in energy analytics techniques, such as NILM, in both academia [12,1,16] and industry [8]. NILM-based energy analytics have been used in different scenarios, such as opportunistic load scheduling for capping peak demand [6], learning thermostats schedule [13], etc.…”

Section: Related Workmentioning

confidence: 99%

Non-intrusive model derivation

Iyengar

Irwin

Shenoy

2016

Proceedings of the Seventh International Conference on Future Energy Systems

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A variety of energy management and analytics techniques rely on models of the power usage of a device over time. Unfortunately, the models employed by these techniques are often highly simplistic, such as modeling devices as simply being on with a fixed power usage or off and consuming little power. As we show, even the power usage of relatively simple devices exhibits much more complexity than a simple on and off state. To address the problem, we present a Non-Intrusive Model Derivation (NIMD) algorithm to automate modeling of residential electric loads using concepts from power systems, statistics, and machine learning. NIMD automatically derives a compact representation of the time-varying power usage of any residential electrical load, including both the device's energy usage and its pattern of usage over time. Such models are useful for a variety of analytics techniques, such as Non-Intrusive Load Monitoring, that have relied on simple on-off models in the past. We evaluate the accuracy of our models by comparing them with both actual ground truth data, and against models that have been designed manually by human experts. We show that models derived via NIMD are comparable in accuracy to models built by experts and closely approximate the ground truth data.

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Section: Related Workmentioning

confidence: 99%

Non-intrusive model derivation

Iyengar

Irwin

Shenoy

2016

Proceedings of the Seventh International Conference on Future Energy Systems

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“…Further, we discuss the different works in NILM in recent years, which have been decisive in driving the research towards an improved load monitoring system. These works include hardware approaches, software and middleware design as well as algorithm analysis for load identification [10][11][12]. The second approach for ALM is gaining popularity in terms of Intrusive Load Monitoring (ILM).…”

Section: Introductionmentioning

confidence: 99%

Smart Plugs: Paradigms and Applications in the Smart City-and-Smart Grid

Suryadevara

Biswal

2019

Energies

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In the current energy ecosystem, the need for a Hybrid Appliance Load Monitoring System (HALMS) to establish a smarter grid and energy infrastructure is undeniable. The increasing popularity of the Internet of Things (IoT) has suddenly pushed the demand for smart and connected devices. This review introduces the term smart plug as a device that uses IoT for establishing HALMS. These smart plugs are a handy solution to make the so-called ‘dumb’ devices smart. The strategy of smart plugs to enhance the energy management experience in connected spaces is presented. This study extensively highlights the current smart plug technologies and the relevant activities and limitations that need to overcome the requirements of HALMS.

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Scalable Data-driven Modeling and Analytics for Smart Buildings

Iyengar¹

2019

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Energy and IOTAn engineer's perspective

Cited by 3 publications

References 0 publications

Non-intrusive model derivation

Non-intrusive model derivation

Smart Plugs: Paradigms and Applications in the Smart City-and-Smart Grid

Scalable Data-driven Modeling and Analytics for Smart Buildings

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