A Multiple Instance Regression Approach to Electrical Load Disaggregation

Serafini, Luca; Tanoni, Giulia; Principi, Emanuele; Spinsante, Susanna; Squartini, Stefano

doi:10.23919/eusipco55093.2022.9909747

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Transfer learning typically requires collecting new data directly from the target environment to fine-tune pretrained models, requiring the engagement of end users for data annotation, which is made simpler by weak supervision. Previous studies have demonstrated the effectiveness of weak labels in improving performance in both disaggregation [24] and multilabel appliance classification tasks [7], [13], and this study proposes using weak labels to jointly distil knowledge and reduce network complexity during transfer learning. The method uses a Convolutional Recurrent Neural Network (CRNN), which has been successfully used in a centralized NILM scenario [7].…”

Section: Introductionmentioning

confidence: 99%

Knowledge Distillation for Scalable Nonintrusive Load Monitoring

Tanoni,

Stankovic,

Stankovic

et al. 2024

IEEE Trans. Ind. Inf.

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Smart meters allow the grid to interface with individual buildings and extract detailed consumption information using nonintrusive load monitoring (NILM) algorithms applied to the acquired data. Deep neural networks, which represent the state of the art for NILM, are affected by scalability issues since they require high computational and memory resources, and by reduced performance when training and target domains mismatched. This article proposes a knowledge distillation approach for NILM, in particular for multilabel appliance classification, to reduce model complexity and improve generalization on unseen data domains. The approach uses weak supervision to reduce labeling effort, which is useful in practical scenarios. Experiments, conducted on U.K.-DALE and RE-FIT datasets, demonstrated that a low-complexity network can be obtained for deployment on edge devices while maintaining high performance on unseen data domains. The proposed approach outperformed benchmark methods in unseen target domains achieving a F 1 -score 0.14 higher than a benchmark model 78 times more complex.

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Section: Introductionmentioning

confidence: 99%

Knowledge Distillation for Scalable Nonintrusive Load Monitoring

Tanoni,

Stankovic,

Stankovic

et al. 2024

IEEE Trans. Ind. Inf.

Self Cite

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“…To reduce the requirement for labelled data, approaches based on semi-supervised learning have been proposed recently [36][37][38]. A different approach to reducing the labelling effort has been proposed in [39,40], where a weakly supervised method is demonstrated to be more effective than the semi-supervised one [38]. Weak supervision allows a lightened data annotation since labels are required in a coarser form [25].…”

Section: Introductionmentioning

confidence: 99%

A weakly supervised active learning framework for non-intrusive load monitoring

Tanoni,

Sobot,

Principi

et al. 2024

Integrated Computer-Aided Engineering

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Energy efficiency is at a critical point now with rising energy prices and decarbonisation of the residential sector to meet the global NetZero agenda. Non-Intrusive Load Monitoring is a software-based technique to monitor individual appliances inside a building from a single aggregate meter reading and recent approaches are based on supervised deep learning. Such approaches are affected by practical constraints related to labelled data collection, particularly when a pre-trained model is deployed in an unknown target environment and needs to be adapted to the new data domain. In this case, transfer learning is usually adopted and the end-user is directly involved in the labelling process. Unlike previous literature, we propose a combined weakly supervised and active learning approach to reduce the quantity of data to be labelled and the end user effort in providing the labels. We demonstrate the efficacy of our method comparing it to a transfer learning approach based on weak supervision. Our method reduces the quantity of weakly annotated data required by up to 82.6–98.5% in four target domains while improving the appliance classification performance.

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“…Recent NILM research has advanced towards adaptive and intelligent energy management systems, integrating machine learning [29] and IoT technologies [30] to enhance system accuracy and expand use in residential and industrial settings. Methods like electrical quantities [31][32][33], power theories [34][35][36], and harmonic decomposition [37,38] are widely used. This paves the way for more sustainable energy use.…”

mentioning

confidence: 99%

Enhancing Residential Electricity Safety and Management: A Novel Non-Intrusive Load Monitoring-Based Methodology for Accurate Appliance Operational State Identification

Liu,

Wang,

et al. 2024

Applied Sciences

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Non-intrusive load monitoring (NILM) technology, crucial for intelligent electricity management, has gained considerable attention in residential electricity usage studies. NILM enables monitoring of total electrical current and voltage in homes, offering insights vital for enhancing safety and preventing domestic electrical accidents. Despite its importance, accurately discerning the operational status of appliances using non-intrusive methods remains a challenging area within this field. This paper presents a novel methodology that integrates an advanced clustering algorithm with a Bayesian network for the identification of appliance operational states. The approach involves capturing the electrical current signals during appliance operation via NILM, followed by their decomposition into odd harmonics. An enhanced clustering algorithm is then employed to ascertain the central coordinates of the signal clusters. Building upon this, a three-layer Bayesian network inference model, incorporating leak nodes, is developed. Within this model, harmonic signals are used as conditions for node activation. The operational states of the appliances are subsequently determined through probabilistic reasoning. The proposed method’s effectiveness is validated through a series of simulation experiments conducted in a laboratory environment. The results of these experiments (low mode 89.1%, medium mode 94.4%, high mode 92.0%, and 98.4% for combination) provide strong evidence of the method’s accuracy in inferring the operational status of household electrical appliances based on NILM technology.

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A Multiple Instance Regression Approach to Electrical Load Disaggregation

Cited by 7 publications

References 19 publications

Knowledge Distillation for Scalable Nonintrusive Load Monitoring

Knowledge Distillation for Scalable Nonintrusive Load Monitoring

A weakly supervised active learning framework for non-intrusive load monitoring

Enhancing Residential Electricity Safety and Management: A Novel Non-Intrusive Load Monitoring-Based Methodology for Accurate Appliance Operational State Identification

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