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

Henao, Nilson; Agbossou, Kodjo; Kélouwani, Sousso; Hosseini, Sayed Saeed; Fournier, Michaël

doi:10.3390/en11010088

Cited by 13 publications

(8 citation statements)

References 35 publications

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“…Household energy-intensive appliances are commonly finitestate loads that can be subject to malfunction at any operation state [35]. Nevertheless, from the perspective of operationtime anomaly detection, these appliances can be classified as two-state operation-time (ON/OFF) loads.…”

Section: B Overview Of Appliance Candidatementioning

confidence: 99%

A Practical Approach to Residential Appliances on-Line Anomaly Detection: A Case Study of Standard and Smart Refrigerators

et al. 2020

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Section: B Overview Of Appliance Candidatementioning

confidence: 99%

A Practical Approach to Residential Appliances on-Line Anomaly Detection: A Case Study of Standard and Smart Refrigerators

et al. 2020

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“…The NILM (named also energy disaggregation) and appliance identification tasks are among the principal applications of such schemes. A large number of papers based on these techniques can be found in the literature such as [81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97].…”

Section: Data Fusion Techniques (F)mentioning

confidence: 99%

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Himeur,

Alsalemi,

Al-Kababji

et al. 2020

Preprint

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Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-theart data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored.Keywords Data fusion • energy efficiency • sensors • appliance identification • fusion of 2D descriptors • machine learning.

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“…Especially, the limitations of high sampling rate data acquisition are more expensive cost and more complex in signature database management [28,29]. However, a more accurate and precise analysis can be provided with more information and assistance [30]. Hence, using a low-frequency sampling rate is more promising in event detection appliances based on analysis the active power and/or reactive power.…”

Section: Related Workmentioning

confidence: 99%

Non-Intrusive Load Monitoring Based on Novel Transient Signal in Household Appliances with Low Sampling Rate

Kim

2018

Energies

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Nowadays climate change problems have been more and more concerns and urgent in the real world. Especially, the energy power consumption monitoring is a considerate trend having positive effects in decreasing affecting climate change. Non-Intrusive Load Monitoring (NILM) is the best economic solution to solve the electrical consumption monitoring issue. NILM captures the electrical signals from the aggregate energy consumption, feature extraction from these signals and then learning and predicting the switch ON/OFF of appliances used these feature extracted. This paper proposed a NILM framework including data acquisition, data feature extraction, and classification model. The main contribution is to develop a new transient signal in a different aspect. The proposed transient signal is extracted from the active power signal in the low-frequency sampling rate. This transient signal is used to detect the event of household appliances. In household appliances event detection, we applied to Decision Tree and Long Short-Time Memory (LSTM) models. The average accuracies of these models achieved 92.64% and 96.85%, respectively. The computational and result experiments present the solution effectiveness for the accurate transient signal extraction in the electrical input signals.

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

Cited by 13 publications

References 35 publications

A Practical Approach to Residential Appliances on-Line Anomaly Detection: A Case Study of Standard and Smart Refrigerators

A Practical Approach to Residential Appliances on-Line Anomaly Detection: A Case Study of Standard and Smart Refrigerators

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Non-Intrusive Load Monitoring Based on Novel Transient Signal in Household Appliances with Low Sampling Rate

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