Crosstalk Suppression in Semi-Intrusive Load Monitoring Systems Using Hall Effect Sensors

Langevin, Antoine; Gagnon, Ghyslain; Cheriet, Mohamed

doi:10.1109/tsg.2020.3002668

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…Yang et al in [13] developed a semi-supervised NILM technique for a multilabel classification problem which occurs mainly due to simultaneous operation of appliances. In [14], hall effect sensors were installed on wires connecting appliances, which reduces the estimation error. However, the installation of additional sensors increases the solution cost.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Head Convolutional Neural Network Based Non-Intrusive Load Monitoring Algorithm Under Dynamic Grid Voltage Conditions

Grover¹,

Panwar²,

Verma³

et al. 2022

Preprint

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In recent times, non-intrusive load monitoring (NILM) has emerged as an important tool for distribution-level energy management systems owing to its potential for energy conservation and management. However, load monitoring in smart building environments is challenging due to high variability of real-time load and varied load composition. Furthermore, as the volume and dimensionality of smart meter's data increases, accuracy and computational time are key concerning factors. In view of these challenges, this paper proposes an improved NILM technique using multi-head (Mh-Net) convolutional neural network (CNN) under dynamic grid voltage conditions. An attention layer is introduced into the proposed CNN model, which helps in improving estimation accuracy of appliance power consumption. The performance of the developed model has been verified on an experimental laboratory setup for multiple appliance sets with varied power consumption levels, under dynamic grid voltages. Moreover, the effectiveness of the proposed model has been verified on widely used UK-DALE data, and its performance has been compared with existing NILM techniques. Results depict that the proposed model accurately identifies appliances, power consumptions and their time-of-use even during practical dynamic grid voltage conditions.

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Section: Introductionmentioning

confidence: 99%

A Multi-Head Convolutional Neural Network Based Non-Intrusive Load Monitoring Algorithm Under Dynamic Grid Voltage Conditions

Grover¹,

Panwar²,

Verma³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the accuracy of measurement is a challenge for NILM. Semi-intrusive load monitoring (SILM) is a compromise of ILM and NILM in both accuracy of data and user experience, which uses SM outside the building and multiple cheap plugand-play monitoring sensors attached to circuit breakers or sockets inside the building [3].…”

Section: Introductionmentioning

confidence: 99%

A Belief Propagation Based State Estimator for Semi-Intrusive Load Monitoring System

Duan

Liu

2022

IEEE Access

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For industrial application of load monitoring techniques, it is important to establish high-performance state estimators of low-cost and low frequency smart meters (SM) and sensors in a power system, which can run under resource-constrained computing units. Because household electronic appliances often tap power from fixed sockets, a finite state table for the corresponding sensors is suitable and convenient. However, SM in the main line may have an enormous state table. In this study, we propose a belief propagation (BP) algorithm to calculate the power consumption of electronic appliances in a semi-intrusive load monitoring (SILM) system whose SM and sensors have state tables with sizes varying largely. The novelty of the proposed method lies in a continuous approximation to a large state table and a switching scheme between discrete and continuous parts of the SILM system. With datasets from numerical simulations and a real-world experimental SILM system in a set of high-density school buildings within a secondary distribution network, the proposed BP algorithm is compared with relevant state-of-the-art algorithms. The results show that the proposed algorithm achieves a percentage of error (8%), which outperforms the percentage achieved by the other methods, a linear state estimation of 99%, a hidden Markov model of 21%, and a full-discrete BP algorithm of 11%. In addition, the complexity of the proposed algorithm is the least of all methods, and the proposed algorithm can run by SoC on concentrators.

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“…SILM methods inherit the advantages of ILM and NILM and potentially allow for an optimal trade-off between the computational complexity, accuracy and cost [11,20]. Evaluating the performance of SILM and NILM with real datasets prove that utilizing a very limited number of meters, the SILM approach significantly improves the accuracy of power disaggregation for large-scale and multi-mode appliances [21].…”

Section: Introductionmentioning

confidence: 99%

Quantification of Disaggregation Difficulty with Respect to the Number of Meters

Azizi,

Beheshti,

Bolouki

2021

Preprint

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A promising approach toward efficient energy management is non-intrusive load monitoring (NILM), that is to extract the consumption profiles of appliances within a residence by analyzing the aggregated consumption signal. Among efficient NILM methods are event-based algorithms in which events of the aggregated signal are detected and classified in accordance with the appliances causing them. The large number of appliances and the presence of appliances with close consumption values are known to limit the performance of event-based NILM methods. To tackle these challenges, one could enhance the feature space which in turn results in extra hardware costs, installation complexity, and concerns regarding the consumer's comfort and privacy. This has led to the emergence of an alternative approach, namely semi-intrusive load monitoring (SILM), where appliances are partitioned into blocks and the consumption of each block is monitored via separate power meters. While a greater number of meters can result in more accurate disaggregation, it increases the monetary cost of load monitoring, indicating a trade-off that represents an important gap in this field. In this paper, we take a comprehensive approach to close this gap by establishing a so-called notion of "disaggregation difficulty metric (DDM)," which quantifies how difficult it is to monitor the events of any given group of appliances based on both their power values and the consumer's usage behavior. Thus, DDM in essence quantifies how much is expected to be gained in terms of disaggregation accuracy of a generic event-based algorithm by installing meters on the blocks of any partition of the appliances. Experimental results based on the REDD dataset illustrate the practicality of the proposed approach in addressing the aforementioned trade-off.

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Crosstalk Suppression in Semi-Intrusive Load Monitoring Systems Using Hall Effect Sensors

Cited by 7 publications

References 29 publications

A Multi-Head Convolutional Neural Network Based Non-Intrusive Load Monitoring Algorithm Under Dynamic Grid Voltage Conditions

A Multi-Head Convolutional Neural Network Based Non-Intrusive Load Monitoring Algorithm Under Dynamic Grid Voltage Conditions

A Belief Propagation Based State Estimator for Semi-Intrusive Load Monitoring System

Quantification of Disaggregation Difficulty with Respect to the Number of Meters

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