Genetic algorithm for pattern detection in NIALM systems

Baranski, Marc; Voss, Juergen

doi:10.1109/icsmc.2004.1400878

Cited by 99 publications

(67 citation statements)

References 5 publications

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“…It is injected into the system as a real valued signal, and converted to an electrical load by a variable resistor in the programmable load. (Like several other residential NILM studies [10,4,22], we focus on real power consumption.) The programmable load is supplied by the utility and potentially the battery, which sit in parallel on the circuit.…”

Section: Full System Simulationmentioning

confidence: 99%

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Protecting consumer privacy from electric load monitoring

McLaughlin

McDaniel

Aiello

2011

Proceedings of the 18th ACM Conference on Computer and Communications Security

206

157

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The smart grid introduces concerns for the loss of consumer privacy; recently deployed smart meters retain and distribute highly accurate profiles of home energy use. These profiles can be mined by Non Intrusive Load Monitors (NILMs) to expose much of the human activity within the served site. This paper introduces a new class of algorithms and systems, called Non-Intrusive Load Leveling (NILL) to combat potential invasions of privacy. NILL uses an in-residence battery to mask variance in load on the grid, thus eliminating exposure of the appliance-driven information used to compromise consumer privacy. We use real residential energy use profiles to drive four simulated deployments of NILL. The simulations show that NILL exposes only 1.1 to 5.9 useful energy events per day hidden amongst hundreds or thousands of similar batterysuppressed events. Thus, the energy profiles exhibited by NILL are largely useless for current NILM algorithms. Surprisingly, such privacy gains can be achieved using battery systems whose storage capacity is far lower than the residence's aggregate load average. We conclude by discussing how the costs of NILL can be offset by energy savings under tiered energy schedules.

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Section: Full System Simulationmentioning

confidence: 99%

“…Thus, NILL will discharge the battery to partially supply the load created by the appliance, maintaining the target load. 4 Similarly, if an appliance enters the OFF state, the load profile will decrease below the target load. These opportunities are used to charge the battery while restoring the target load.…”

Section: Non-intrusive Load Levelingmentioning

confidence: 99%

Protecting consumer privacy from electric load monitoring

McLaughlin

McDaniel

Aiello

2011

Proceedings of the 18th ACM Conference on Computer and Communications Security

206

157

View full text Add to dashboard Cite

show abstract

“…For example, Baranski and Voss (2004b) generate finite-state-machine models of appliances using a genetic algorithm that combines events (steps in real power detected via a standard electricity meter) that occur a percentage of the time larger than a given threshold. They then run a clustering algorithm to assign readings obtained over one day to appliance types by their real power.…”

Section: Appendix B Description Of Published Algorithmsmentioning

confidence: 99%

Is disaggregation the holy grail of energy efficiency? The case of electricity

Gupta²,

et al. 2013

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This paper aims to address two timely energy problems. First, significant low-cost energy reductions can be made in the residential and commercial sectors, but these savings have not been achievable to date. Second, billions of dollars are being spent to install smart meters, yet the energy saving and financial benefits of this infrastructure -without careful consideration of the human element -will not reach its full potential. We believe that we can address these problems by strategically marrying them, using disaggregation. Disaggregation refers to a set of statistical approaches for extracting end-use and/or appliance level data from an aggregate, or wholebuilding, energy signal. In this paper, we explain how appliance level data affords numerous benefits, and why using the algorithms in conjunction with smart meters is the most cost-effective and scalable solution for getting this data. We review disaggregation algorithms and their requirements, and evaluate the extent to which smart meters can meet those requirements. Research, technology, and policy recommendations are also outlined.

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“…The authors of NILM-Eval have also thoroughly tested their systems' ability to evaluate and benchmark disaggregation algorithms. To this end, they used their own dataset (ECO) to evaluate four different algorithms, two of them event-based (Baranski & Voss, 2004) and (Weiss, Helfenstein, Mattern, & Staake, 2012) and two event-less (Kolter & Jaakkola, 2012;Parson, Ghosh, Weal, & Rogers, 2012). These algorithms were tested under different parameter configurations, and the results reported using the system default performance metrics (P, R, RMSE, and Dev).…”

Section: Nilm-evalmentioning

confidence: 99%

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

Pereira¹,

Nunes

2018

WIREs Data Min & Knowl

139

103

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Non-intrusive load monitoring (also known as NILM or energy disaggregation) is the process of estimating the energy consumption of individual appliances from electric power measurements taken at a limited number of locations in the electric distribution of a building. This approach reduces sensing infrastructure costs by relying on machine learning techniques to monitor electric loads. However, the ability to evaluate and benchmark the proposed approaches across different datasets is key for enabling the generalization of research findings and consequently contributes to the large-scale adoption of this technology. Still, only recently researchers have focused on creating and standardizing the existing datasets in order to deliver a single interface to run NILM evaluations. Furthermore, there is still no consensus regarding, which performance metrics should be used to measure and report the performance of NILM systems and their underlying algorithms. This paper provides a review of the main datasets, metrics, and tools for evaluating the performance of NILM systems and technologies. Specifically, we review three main topics: (a) publicly available datasets, (b) performance metrics, and (c) frameworks and toolkits. The review suggests future research directions in NILM systems and technologies, including cross-datasets, performance metrics for evaluation and generalizable frameworks for benchmarking NILM technology.

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Genetic algorithm for pattern detection in NIALM systems

Cited by 99 publications

References 5 publications

Protecting consumer privacy from electric load monitoring

Protecting consumer privacy from electric load monitoring

Is disaggregation the holy grail of energy efficiency? The case of electricity

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

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