3,089 research outputs found

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

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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 non-intrusive 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

    Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation

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    In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.Peer reviewedFinal Published versio

    Robust energy disaggregation using appliance-specific temporal contextual information

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    An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time-varying power consumption patterns in low-frequency datasets. The proposed methodology was evaluated using datasets of different sampling frequency, number and type of appliances. When employing appliance-specific temporal contextual information, an improvement of 1.5% up to 7.3% was observed. With the two-stage disaggregation architecture and using appliance-specific temporal contextual information, the overall energy disaggregation accuracy was further improved across all evaluated datasets with the maximum observed improvement, in terms of absolute increase of accuracy, being equal to 6.8%, thus resulting in a maximum total energy disaggregation accuracy improvement equal to 10.0%.Peer reviewedFinal Published versio

    NILM techniques for intelligent home energy management and ambient assisted living: a review

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    The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

    Energy Disaggregation Using Elastic Matching Algorithms

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    © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.Peer reviewedFinal Published versio

    Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

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    Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Recently, deep neural networks have driven remarkable improvements in classification performance in neighbouring machine learning fields such as image classification and automatic speech recognition. In this paper, we adapt three deep neural network architectures to energy disaggregation: 1) a form of recurrent neural network called `long short-term memory' (LSTM); 2) denoising autoencoders; and 3) a network which regresses the start time, end time and average power demand of each appliance activation. We use seven metrics to test the performance of these algorithms on real aggregate power data from five appliances. Tests are performed against a house not seen during training and against houses seen during training. We find that all three neural nets achieve better F1 scores (averaged over all five appliances) than either combinatorial optimisation or factorial hidden Markov models and that our neural net algorithms generalise well to an unseen house.Comment: To appear in ACM BuildSys'15, November 4--5, 2015, Seou

    Non-Intrusive Load Monitoring in the OpenHAB Smart Home Framework

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    Non-intrusive load monitoring (NILM) on elektrienergia tarbimise jälgimise meetod, milles rakendatakse masinõppe meetodeid, et automaatselt vooluvõrku ühendatud seadmete kogutarbest eraldada üksikute seadmete energiatarve. Käesolev bakalaureusetöö kirjeldab reaalajas töötavat NILM lahendust, mis saab sisendina kasutatavad energiatarbimise andmed targa kodu automatiseerimise keskkonnast openHAB. Kogu energiatarbimise disagregeerimiseks kasutatakse vabavaralist tööriista NILMTK, mis võimaldab kogu süsteemi energiatarbimisest üksikute seadmete tarbimist eraldada. Tuvastatud seadmete hetketarbimised saadetakse tagasi openHABi keskkonda, kus neid võib kasutada koduautomaatikas. Arvutiteaduse instituudi värkvõrgu laboris läbi viidud testide tulemused näitavad, et lahendus suudab täpselt tuvastada stabiilse ja suure energiatarbimisega seadmeid (näiteks soojapuhur), kuid on ebatäpne selliste seadmete eristamisel, mille energiatarbimine kõigub palju (näiteks kohvimasin) või moodustab kogutarbimisest vaid väikse osa (näiteks pirn).Non-intrusive load monitoring (NILM) is an approach to energy monitoring, where machine learning techniques are applied to data from a single energy meter to determine the energy consumption of each appliance connected to the local electric network. This thesis presents a real-time NILM solution for the smart home that relies on the home automation platform openHAB for live power readings. NILMTK – an open-source energy disaggregation toolkit – is used to break down the aggregate live energy consumption data to the appliance level in real time. The disaggregated power data are then sent back to openHAB, where they can be displayed to the user and enable further automation. Tests conducted at the University of Tartu Internet of Things and Smart Solutions laboratory indicate high recognition accuracy for appliances with a steady high energy demand (e.g. a space heater), while lower accuracy scores were reported for appliances with a fluctuating power demand (e.g. a coffee maker) and lowpowered appliances that only make up a small proportion of the total energy consumption (e.g. a light bulb)
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