Electricity Demand Activation Extraction

Laviron, Pauline; Dai, Xueqi; Huquet, Bérénice; Palpanas, Themis

doi:10.1145/3447555.3464865

Cited by 11 publications

(7 citation statements)

References 15 publications

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“…Furthermore, NILM approaches can be divided into supervised and unsupervised learning, depending on whether they usee labeled data for training the models. Supervised learning involves classifying detected events (appliances being switched on or off) by matching extracted features [33,36,45,57]. In contrast, unsupervised NILM methods detect events by analyzing feature similarities, or correlations without using labeled data [20,60].…”

Section: Non-intrusive Load Monitoring (Nilm) and Appliance Detectionmentioning

confidence: 99%

“…Since device recognition can be seen as a step of NILM-based methods, different approaches exist in the literature to detect appliances in load curves using high or low-frequency smart meter data [4,27,28,33,45,47,57]. However, numerous studies using pattern recognition at low frequency require knowledge about how each device operates.…”

Section: Non-intrusive Load Monitoring (Nilm) and Appliance Detectionmentioning

confidence: 99%

“…This problem is closely related to Non-Intrusive Load Monitoring (NILM), which aims to identify the power consumption, pattern, or on/off state activation of individual appliances using only the total consumption series [29]. While detecting an appliance can be seen as a step in NILM-based methods [4,27,28,33,45,47,57], and diverse approaches have been proposed in the literature [4,27,28,33,45,47,57], they differ from our objective. Indeed, these studies essentially focus on detecting when a specific appliance is "ON" rather than if a household owns a specific appliance, and the presence of a specific appliance is in several cases already known before applying these approaches.…”

Section: Introductionmentioning

confidence: 99%

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Appliance Detection Using Very Low-Frequency Smart Meter Time Series

Petralia,

Charpentier,

Boniol

et al. 2023

Proceedings of the 14th ACM International Conference on Future Energy Systems

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In recent years, smart meters have been widely adopted by electricity suppliers to improve the management of the smart grid system. These meters usually collect energy consumption data at a very low frequency (every 30min), enabling utilities to bill customers more accurately. To provide more personalized recommendations, the next step is to detect the appliances owned by customers, which is a challenging problem, due to the very-low meter reading frequency. Even though the appliance detection problem can be cast as a time series classification problem, with many such classifiers having been proposed in the literature, no study has applied and compared them on this specific problem. This paper presents an in-depth evaluation and comparison of state-of-the-art time series classifiers applied to detecting the presence/absence of diverse appliances in very low-frequency smart meter data. We report results with five real datasets. We first study the impact of the detection quality of 13 different appliances using 30min sampled data, and we subsequently propose an analysis of the possible detection performance gain by using a higher meter reading frequency. The results indicate that the performance of current time series classifiers varies significantly. Some of them, namely deep learning-based classifiers, provide promising results in terms of accuracy (especially for certain appliances), even using 30min sampled data, and are scalable to the large smart meter time series collections of energy consumption data currently available to electricity suppliers. This paper appeared in ACM e-Energy 2023. CCS CONCEPTS• Computing methodologies → Learning paradigms.

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Section: Non-intrusive Load Monitoring (Nilm) and Appliance Detectionmentioning

confidence: 99%

Section: Non-intrusive Load Monitoring (Nilm) and Appliance Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Appliance Detection Using Very Low-Frequency Smart Meter Time Series

Petralia,

Charpentier,

Boniol

et al. 2023

Proceedings of the 14th ACM International Conference on Future Energy Systems

Self Cite

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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. First, the vast majority of NILM studies relied on smart meter data recorded at 1Hz (or more), which is much more detailed than the datasets available in practice.…”

Section: Related Work and Problem Definition 21 Appliance Detectionmentioning

confidence: 99%

“…Appliance detection has become an important area of research [15,34,42,44]. Signature-based methods are widely adopted and use information related to the unique patterns of specific appliances [30]. However, most of these studies relied on data from smart meters capable of recording one, or more values per second, in contrast to the vast majority of smart meters installed by suppliers that collect data at considerably lower frequencies.…”

Section: Introductionmentioning

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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Privacy Issues in Smart Grid Data: From Energy Disaggregation to Disclosure Risk

Adewole

Torra

2022

Lecture Notes in Computer Science

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Electricity Demand Activation Extraction

Cited by 11 publications

References 15 publications

Appliance Detection Using Very Low-Frequency Smart Meter Time Series

Appliance Detection Using Very Low-Frequency Smart Meter Time Series

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

Privacy Issues in Smart Grid Data: From Energy Disaggregation to Disclosure Risk

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