ACS-F2 &amp;#x2014; A new database of appliance consumption signatures

Ridi, Antonio; Gisler, Christophe; Hennebert, Jean

doi:10.1109/socpar.2014.7007996

Cited by 42 publications

(28 citation statements)

References 8 publications

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“…Only two other datasets (WHITED 12 and COOLL 13 ) contain submetered data sampled at a frequency higher than 1 Hz. All the others, like ACS-F2 14 and Tracebase 15 , contain submetered data sampled at a frequency lower than 1 Hz. From these last datasets, only two, i.e., REDD 16 , and UK-DALE 17 , contain aggregated data sampled at a frequency higher than 1 Hz.…”

Section: Background and Summarymentioning

confidence: 99%

A voltage and current measurement dataset for plug load appliance identification in households

et al. 2020

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This paper presents the Plug-Load Appliance Identification Dataset (PLAID), a labelled dataset containing records of the electrical voltage and current of domestic electrical appliances obtained at a high sampling frequency (30 kHz). The dataset contains 1876 records of individually-metered appliances from 17 different appliance types (e.g., refrigerators, microwave ovens, etc.) comprising 330 different makes and models, and collected at 65 different locations in Pittsburgh, Pennsylvania (USA). Additionally, PLAID contains 1314 records of the combined operation of 13 of these appliance types (i.e., measurements obtained when multiple appliances were active simultaneously). Identifying electrical appliances based on electrical measurements is of importance in demand-side management applications for the electrical power grid including automated load control, load scheduling and nonintrusive load monitoring. This paper provides a systematic description of the measurement setup and dataset so that it can be used to develop and benchmark new methods in these and other applications, and so that extensions to it can be developed and incorporated in a consistent manner. 110 130 max 2 0 RMSThe main contributions of this paper are that it:The complete PLAID dataset and all mentioned scripts are available in 5 . In the same repository, code written to capture the data can be found. The files are two scripts, namely 'collecting_data.vi' (written with LabVIEW) and 'collecting_data.m' (written in MATLAB).

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Section: Background and Summarymentioning

confidence: 99%

A voltage and current measurement dataset for plug load appliance identification in households

et al. 2020

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“…In previous works we presented two versions of the Appliance Consumption signature -Fribourg (ACS-F) database: ACS-F1 and the ACS-F2 [12] [13]. Compared to other databases publicly available, the ACS-F has two peculiarities:…”

Section: Acs-f Databasementioning

confidence: 99%

“…ACS-F2 is the second version of the ACS-F database [13]. It can be considered as an extension of the ACS-F1.…”

Section: B Second Version -Acs-f2mentioning

confidence: 99%

“…The ACS-F2 version contains 450 signatures acquired from 225 appliances uniformly spread into 15 categories: mobile phone, coffee machine, computer station (including monitor), laptop, fridge and freezer, Hi-Fi system (CD players), lamp CFL, incandescent lamp, microwave oven, printer, television (LCD or LED), monitor, kettle, fan and shaver. More information about the acquisition procedure is available in [13].…”

Section: B Second Version -Acs-f2mentioning

confidence: 99%

“…In a second step we released the second version of the database and we presented our initial benchmark using k-NN and GMM for both protocols [13]. We obtained respectively 83.1% and 89.8% for the Intersession protocol and respectively 53.6% and 73.3% for the Unseen Appliance protocol.…”

Section: B Fixed Signal Lengthmentioning

confidence: 99%

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Processing smart plug signals using machine learning

Ridi

Gisler

Hennebert

2015

2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)

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The automatic identification of appliances through the analysis of their electricity consumption has several purposes in Smart Buildings including better understanding of the energy consumption, appliance maintenance and indirect observation of human activities. Electric signatures are typically acquired with IoT smart plugs integrated or added to wall sockets. We observe an increasing number of research teams working on this topic under the umbrella Intrusive Load Monitoring. This term is used as opposition to Non-Intrusive Load Monitoring that refers to the use of global smart meters. We first present the latest evolutions of the ACS-F database, a collections of signatures that we made available for the scientific community. The database contains different brands and/or models of appliances with up to 450 signatures. Two evaluation protocols are provided with the database to benchmark systems able to recognise appliances from their electric signature. We present in this paper two additional evaluation protocols intended to measure the impact of the analysis window length. Finally, we present our current best results using machine learning approaches on the 4 evaluation protocols.

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Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

Pereira¹,

Nunes

2018

WIREs Data Min & Knowl

139

103

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Non-intrusive load monitoring (also known as NILM or energy disaggregation) is the process of estimating the energy consumption of individual appliances from electric power measurements taken at a limited number of locations in the electric distribution of a building. This approach reduces sensing infrastructure costs by relying on machine learning techniques to monitor electric loads. However, the ability to evaluate and benchmark the proposed approaches across different datasets is key for enabling the generalization of research findings and consequently contributes to the large-scale adoption of this technology. Still, only recently researchers have focused on creating and standardizing the existing datasets in order to deliver a single interface to run NILM evaluations. Furthermore, there is still no consensus regarding, which performance metrics should be used to measure and report the performance of NILM systems and their underlying algorithms. This paper provides a review of the main datasets, metrics, and tools for evaluating the performance of NILM systems and technologies. Specifically, we review three main topics: (a) publicly available datasets, (b) performance metrics, and (c) frameworks and toolkits. The review suggests future research directions in NILM systems and technologies, including cross-datasets, performance metrics for evaluation and generalizable frameworks for benchmarking NILM technology.

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ACS-F2 — A new database of appliance consumption signatures

Cited by 42 publications

References 8 publications

A voltage and current measurement dataset for plug load appliance identification in households

A voltage and current measurement dataset for plug load appliance identification in households

Processing smart plug signals using machine learning

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

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