Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring

Kaselimi, Maria; Protopapadakis, Eftychios; Voulodimos, Athanasios; Doulamis, Nikolaos; Doulamis, Anastasios

doi:10.3390/s22155872

Cited by 59 publications

(28 citation statements)

References 133 publications

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“…In general, it is widely accepted that good generalization is one of the crucial characteristics that the proposed NILM algorithm should have and is highlighted as one of the five main challenges in [ 46 ]. Due to that fact, most of the research tested the proposed solutions on data from houses that were not seen during the training process.…”

Section: Problem Definitionmentioning

confidence: 99%

A Semi-Supervised Approach for Improving Generalization in Non-Intrusive Load Monitoring

Pujić

Tomašević

Batić

2023

Sensors

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Non-intrusive load monitoring (NILM) considers different approaches for disaggregating energy consumption in residential, tertiary, and industrial buildings to enable smart grid services. The main feature of NILM is that it can break down the bulk electricity demand, as recorded by conventional smart meters, into the consumption of individual appliances without the need for additional meters or sensors. Furthermore, NILM can identify when an appliance is in use and estimate its real-time consumption based on its unique consumption patterns. However, NILM is based on machine learning methods and its performance is dependent on the quality of the training data for each appliance. Therefore, a common problem with NILM systems is that they may not generalize well to new environments where the appliances are unknown, which hinders their widespread adoption and more significant contributions to emerging smart grid services. The main goal of the presented research is to apply a domain adversarial neural network (DANN) approach to improve the generalization of NILM systems. The proposed semi-supervised algorithm utilizes both labeled and unlabeled data and was tested on data from publicly available REDD and UK-DALE datasets. The results show a 3% improvement in generalization performance on highly uncorrelated data, indicating the potential for real-world applications.

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Section: Problem Definitionmentioning

confidence: 99%

A Semi-Supervised Approach for Improving Generalization in Non-Intrusive Load Monitoring

Pujić

Tomašević

Batić

2023

Sensors

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“…Appliance detection is a problem related to Non-Intrusive Load Monitoring (NILM), which aims at identifying the power consumption, pattern, or on/off state activation of individual appliances using only the total consumption series [29]. Even though detecting an appliance can be seen as a step of NILM-based methods [4,27,28,30,39,43,50], they differ from our objective for two main reasons.…”

Section: Related Work and Problem Definition 21 Appliance Detectionmentioning

confidence: 99%

ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series

Petralia,

Charpentier,

Palpanas

2023

Proc. VLDB Endow.

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Over the past decade, millions of smart meters have been installed by electricity suppliers worldwide, allowing them to collect a large amount of electricity consumption data, albeit sampled at a low frequency (one point every 30min). One of the important challenges these suppliers face is how to utilize these data to detect the presence/absence of different appliances in the customers' households. This valuable information can help them provide personalized offers and recommendations to help customers towards the energy transition. Appliance detection can be cast as a time series classification problem. However, the large amount of data combined with the long and variable length of the consumption series pose challenges when training a classifier. In this paper, we propose ADF, a framework that uses subsequences of a client consumption series to detect the presence/absence of appliances. We also introduce TransApp, a Transformer-based time series classifier that is first pretrained in a self-supervised way to enhance its performance on appliance detection tasks. We test our approach on two real datasets, including a publicly available one. The experimental results with two large real datasets show that the proposed approach outperforms current solutions, including state-of-the-art time series classifiers applied to appliance detection.

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“…N ON-Intrusive Load Monitoring (NILM) refers to the process of analyzing the aggregated energy consumption of a residential building to infer the individual consumption pattern of domestic appliances [1]. In recent years, NILM approaches have transversed from statistical analysis methods to deep learning techniques due to their superior performance capabilities.…”

Section: Introductionmentioning

confidence: 99%

Performance-Aware NILM Model Optimization for Edge Deployment

Sykiotis¹,

Athanasoulias²,

Kaselimi³

et al. 2023

IEEE Trans. on Green Commun. Netw.

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Non-Intrusive Load Monitoring (NILM) describes the extraction of the individual consumption pattern of a domestic appliance from the aggregated household consumption. Nowadays, the NILM research focus is shifted towards practical NILM applications, such as edge deployment, to accelerate the transition towards a greener energy future. NILM applications at the edge eliminate privacy concerns and data transmission-related problems. However, edge resource restrictions pose additional challenges to NILM. NILM approaches are usually not designed to run on edge devices with limited computational capacity, and therefore model optimization is required for better resource management. Recent works have started investigating NILM model optimization, but they utilize compression approaches arbitrarily without considering the trade-off between model performance and computational cost. In this work, we present a NILM model optimization framework for edge deployment. The proposed edge optimization engine optimizes a NILM model for edge deployment depending on the edge device's limitations and includes a novel performance-aware algorithm to reduce the model's computational complexity. We validate our methodology on three edge application scenarios for four domestic appliances and four model architectures. Experimental results demonstrate that the proposed optimization approach can lead up to a 36.3% average reduction of model computational complexity and a 75% reduction of storage requirements.

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Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring

Cited by 59 publications

References 133 publications

A Semi-Supervised Approach for Improving Generalization in Non-Intrusive Load Monitoring

A Semi-Supervised Approach for Improving Generalization in Non-Intrusive Load Monitoring

ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series

Performance-Aware NILM Model Optimization for Edge Deployment

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