Nonintrusive load monitoring (NILM) performance evaluation

Makonin, Stephen; Popowich, Fred

doi:10.1007/s12053-014-9306-2

Cited by 162 publications

(88 citation statements)

References 7 publications

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“…We compare the performance of our ALIP method with the IP-based disaggregation from [13] in terms of two accuracy measures [17]: per appliance accuracy…”

Section: Resultsmentioning

confidence: 99%

Load Disaggregation Based on Aided Linear Integer Programming

Bhotto

Makonin

Bajić

2017

IEEE Trans. Circuits Syst. II

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Abstract-Load disaggregation based on aided linear integer programming (ALIP) is proposed. We start with a conventional linear integer programming (IP) based disaggregation and enhance it in several ways. The enhancements include additional constraints, correction based on a state diagram, median filtering, and linear programming-based refinement. With the aid of these enhancements, the performance of IP-based disaggregation is significantly improved. The proposed ALIP system relies only on the instantaneous load samples instead of waveform signatures, and hence works well on low-frequency data. Experimental results show that the proposed ALIP system performs better than conventional IP-based load disaggregation.

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“…We compare the performance of our ALIP method with the IP-based disaggregation from [13] in terms of two accuracy measures [17]: per appliance accuracy…”

Section: Resultsmentioning

confidence: 99%

Load Disaggregation Based on Aided Linear Integer Programming

Bhotto

Makonin

Bajić

2017

IEEE Trans. Circuits Syst. II

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“…The accuracy of the ESH estimated power profile is evaluated using the following metric according to [45]:…”

Section: Methodsmentioning

confidence: 99%

Power Estimation of Multiple Two-State Loads Using A Probabilistic Non-Intrusive Approach

et al. 2018

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This paper investigates a non-intrusive approach of retrieving electric space heater (ESH) power profiles from a residential aggregated signal. In cold-climate regions with heating appliances controlled by electronic thermostats, an accurate non-intrusive recognition of power profiles is a challenging task. Accordingly, a robust disaggregation approach based on the difference factorial hidden Markov model (DFHMM) and the Kronecker operation is contributed. The proposed method aims to uncover the underlying stochastic tow-state models of ESHs using their common prior knowledge. The major advantage of the developed load-monitoring architecture consists of modeling simplicity and inference as well as load-detection efficacy in the presence of perturbations from other unknown loads. The experimental results prove the effectiveness of the method in manipulating the challenging case of multiple two-state loads with a high event overlapping probability.

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“…Ei TECA The Total energy correctly assigned (Johnson & Willsky, 2013;Kolter & Matthew, 2011;Makonin & Popowich, 2014) is the total error in assigned energy, normalized by the actual energy consumption in each time slice averaged over all appliances.…”

Section: Feementioning

confidence: 99%

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

Pereira¹,

Nunes

2018

WIREs Data Min & Knowl

133

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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Nonintrusive load monitoring (NILM) performance evaluation

Cited by 162 publications

References 7 publications

Load Disaggregation Based on Aided Linear Integer Programming

Load Disaggregation Based on Aided Linear Integer Programming

Power Estimation of Multiple Two-State Loads Using A Probabilistic Non-Intrusive Approach

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

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