NILM: Multivariate DNN performance analysis with high frequency features

Mariño, Camilo; Masquil, Elías; Marchesoni, Franco; Fernández, Alicia; Massaferro, Pablo

doi:10.1109/isgtlatinamerica52371.2021.9543016

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Relating the elements in S * to (12), the time-varying signal of the appliance can be obtained. When the load profile of one appliance is reconstructed, it is removed from the total power signal to reduce the interference from other appliances.…”

Section: Load Profile Reconstruction Based On Graph Signalmentioning

confidence: 99%

“…Step 2: the combined K-means clustering algorithm is used to cluster the prior information of the load curve of the target equipment, and the threshold vector is established based on (11). Moreover, based on (12), the prior graph signal of the target appliance is established. Different graph signal values reflect different operating states of the equipment.…”

Section: Load Profile Reconstruction Based On Graph Signalmentioning

confidence: 99%

“…Various studies [5] - [9] extract time-domain characteristics of signals such as active power and current harmonics to decompose the single signal from the total power consumption signal. The use of S-transform, wavelet transform, or other transformation methods to extract the frequency-domain characteristics can achieve the effective monitoring of the operation states of loads [10] - [12]. Establishing the V-I trajectory (mutual trajectory of the instantaneous voltage and steady-state current waveforms) of the load can improve the accuracy of load identification [13], [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Non-intrusive Load Monitoring Based on Graph Total Variation for Residential Appliances

Ma,

Zheng,

Deng

et al. 2024

Journal of Modern Power Systems and Clean Energy

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Non-intrusive load monitoring is a technique for monitoring the operating conditions of electrical appliances by collecting the aggregated electrical information at the household power inlet. Despite several studies on the mining of unique load characteristics, few studies have extensively considered the high computational burden and sample training. Based on lowfrequency sampling data, a non-intrusive load monitoring algorithm utilizing the graph total variation (GTV) is proposed in this study. The algorithm can effectively depict the load state without the need for prior training. First, the combined Kmeans clustering algorithm and graph signals are used to build concise and accurate graph structures as load models. The GTV representing the internal structure of the graph signal is introduced as the optimization model and solved using the augmented Lagrangian iterative algorithm. The introduction of the difference operator decreases the computing cost and addresses the inaccurate reconstruction of the graph signal. With low-frequency sampling data, the algorithm only requires a little prior data and no training, thereby reducing the computing cost. Experiments conducted using the reference energy disaggregation dataset and almanac of minutely power dataset demonstrated the stable superiority of the algorithm and its low computational burden.

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Section: Load Profile Reconstruction Based On Graph Signalmentioning

confidence: 99%

Section: Load Profile Reconstruction Based On Graph Signalmentioning

confidence: 99%