Residential Household Non-Intrusive Load Monitoring via Graph-Based Multi-Label Semi-Supervised Learning

Ding, Li; Dick, Scott

doi:10.1109/tsg.2018.2865702

Cited by 86 publications

(54 citation statements)

References 37 publications

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“…A greedy-gradient max cut (GGMC)-based bivariate formulation strategy for GSSL is proposed in [22], and the extension of this strategy for multiclass problems is shown in [23] [25]. Multi-label GSSL-based residential load monitoring is proposed in [24].…”

Section: Introductionmentioning

confidence: 99%

Greedy-Gradient Max Cut-Based Fault Diagnosis for Direct Online Induction Motors

2020

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In this paper, a graph-based semi-supervised learning (GSSL) algorithm, greedy-gradient max cut (GGMC), based fault diagnosis method for direct online induction motors is proposed. Two identical 0.25 HP three-phase squirrel-cage induction motors under healthy, single-and multi-fault conditions were tested in the lab. Three-phase stator currents and three-dimensional vibration signals of the two motors were recorded simultaneously in each test, and used as datasets in this study. Features for machine learning are extracted from experimental stator currents and vibration data by the discrete wavelet transform (DWT). To validate the effectiveness of the proposed GGMC-based fault diagnosis method, its classification accuracy using binary classification and multiclass classification for faults of the two motors are compared with other two GSSL algorithms, local and global consistency (LGC) and Gaussian field and harmonic function (GFHF). In this study, the performance of stator currents and vibration as a monitoring signal is evaluated, it is found that stator currents perform much better than vibration signals for multiclass classification, while they both perform well for binary classification.

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Section: Introductionmentioning

confidence: 99%

Greedy-Gradient Max Cut-Based Fault Diagnosis for Direct Online Induction Motors

2020

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“…A disaggregation model can be trained either as a singletarget [5], [14] or multi-target [15] regression problem, or as a single-label [2], [16]- [18], or multi-label [3], [7], [19]- [22] classification problem. Single-and multi-label classification and multi-class classification were explored in many works in the literature.…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Energy Disaggregation using Convolutional Neural Networks

Ayub¹,

Elsayed²

2020

IJACSA

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Non-Intrusive Load Monitoring (NILM) has become popular for smart meters in recent years due to its low cost installation and maintenance. However, it requires efficient and robust machine learning models to disaggregate the respective electrical appliance energy from the mains. This study investigated NILM in the form of direct point-to-point multiple and single target regression models using convolutional neural networks. Two model architectures have been utilized and compared using five different metrics on two benchmarking datasets (ENERTALK and REDD). The experimental results showed that multi-target disaggregation setting is more complex than single-target disaggregation. For multi-target setting of ENERTALK dataset, the highest individual F1-score is 95.37% and the overall average F1-score is 75.00%. Better results were obtained for the multi-target setting of the other dataset with higher overall average F1-score of 83.32%. Additionally, the robustness and knowledge transfer capability of the models through cross-appliance and cross-domain disaggregation was demonstrated by training for a specific appliance on a specific data, and testing for a different appliance, house and dataset. The proposed models can also disaggregate simultaneous operating appliances with higher F1-scores.

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“…The key motivator for ongoing large-scale smart metering deployments worldwide [1], [2] is to maximise benefits of the smart grid. Smart meter data has been shown to improve grid operation and maintenance of distribution networks [3], fault detection [4], non-technical loss detection [5], outage prediction [6], load forecasting [7], demand response [8] and improving customer satisfaction, including accurate billing and meaningful energy feedback via Non-Intrusive Load Monitoring (NILM), that is, disaggregating the total household consumption down to the load level [9], [10], [11]. Hence, smart meter data analytics are critical to the success of the smart grid [12].…”

Section: Introductionmentioning

confidence: 99%

“…However, most event-based NILM methods disaggregate one appliance at a time, and do not check if the sum of the disaggregated loads is approaching the true measured result, e.g., [19], [20], [33], [34], [29], [35], [36], [37]. This is a preferred approach (vs. disaggregating all appliances at once) since it facilitates transfer learning [38] (i.e., applying the developed appliance models to 'unseen' houses) and avoids multi-class classification [9], [20], [39], [25], [40], which is often less robust to noise. However, disaggregating one appliance at a time, potentially results in significant load over/under estimation [31].…”

Section: Introductionmentioning

confidence: 99%

A Generic Optimisation-Based Approach for Improving Non-Intrusive Load Monitoring

Jakovetić

Zhao

et al. 2019

IEEE Trans. Smart Grid

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The large-scale deployment of smart metering worldwide has ignited renewed interest in electrical load disaggregation, or non-intrusive load monitoring (NILM). Most NILM algorithms disaggregate one appliance at a time, remove the estimated appliance contribution from the total load, and then move on to disaggregate the next appliance. On one hand, this is efficient since multi-class classification is avoided and analytical models for each appliance can be developed independently of other appliances with the benefit of being transferred to unseen houses that have different sets of appliances. On the other hand, however, these methods can significantly under-or over-estimate the total consumption since they do not minimise the difference between the measured aggregate load and the sum of estimated individual loads. Motivated by minimising the latter difference without losing the benefits of existing NILM algorithms, we propose novel post-processing approaches for improving the accuracy of existing NILM. This is posed as an optimisation problem to refine the final NILM result using regularisation, based on the level of confidence in the original NILM output. First, we propose a heuristic method to solve this (combinatorial) boolean quadratic problem through relaxing zero-one constraint sets to compact zero-one intervals. Convex-based solutions, including norm-1, norm-2 and semi-definite programming-based relaxation, are proposed trading off accuracy and complexity. We demonstrate good performance of the proposed post-processing methods, applicable to any event-based NILM, compared with 4 state-of-the-art benchmarks, using public REFIT and REDD electrical load datasets.

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Residential Household Non-Intrusive Load Monitoring via Graph-Based Multi-Label Semi-Supervised Learning

Cited by 86 publications

References 37 publications

Greedy-Gradient Max Cut-Based Fault Diagnosis for Direct Online Induction Motors

Greedy-Gradient Max Cut-Based Fault Diagnosis for Direct Online Induction Motors

Multi-Target Energy Disaggregation using Convolutional Neural Networks

A Generic Optimisation-Based Approach for Improving Non-Intrusive Load Monitoring

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