9,568 research outputs found

    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

    Deep Learning Applications in non-intrusive load monitoring

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    Within the frame of the project Non-Intrusive Load Monitoring for Intelligent Home Energy Management Systems, this work will present a deep learning application in non-intrusive load monitoring on a case study in a residential home in in Gambelas, Faro in the Algarve region south of Portugal. This work has for a goal to detect type 2 appliances in different houses. For the sake of this study, two models will be trained: - Convolutional Neural Network - Long Short-term Memory Recurrent Neural Network on three datasets: - UKDale - REDD - Data from the Portuguese private residential house from the project NILM for IHEMS.No âmbito do projeto Monitorização de Carga Não Intrusiva para Sistemas Inteligentes de Gestão de Energia Doméstica, este trabalho apresentará uma aplicação de aprendizagem profunda na monitorização de carga não intrusiva num estudo de caso numa casa residencial em Gambelas, Faro na região sul do Algarve de Portugal. Este trabalho tem por objetivo detectar eletrodomésticos tipo 2 em diferentes residências. Para fins deste estudo, dois modelos serão treinados: - Rede Neural Convolucional - Rede Neural Recorrente de Memória Longa de Curto Prazo em três conjuntos de dados: - UKDale - REDD - Dados da habitação privada portuguesa do projecto NILM para IHEMS

    Support Vector Machine-Assisted Improvement Residential Load Disaggregation

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    Smart-Building Applications:Deep Learning-Based, Real-Time Load Monitoring

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    Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

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    The useful planning and operation of the energy system requires a sustainability assessment of the system, in which the load model adopted is the most important factor in sustainability assessment. Having information about energy consumption patterns of the appliances allows consumers to manage their energy consumption efficiently. Non-intrusive load monitoring (NILM) is an effective tool to recognize power consumption patterns from the measured data in meters. In this paper, an unsupervised approach based on dimensionality reduction is applied to identify power consumption patterns of home electrical appliances. This approach can be utilized to classify household activities of daily life using data measured from home electrical smart meters. In the proposed method, the power consumption curves of the electrical appliances, as high-dimensional data, are mapped to a low-dimensional space by preserving the highest data variance via principal component analysis (PCA). In this paper, the reference energy disaggregation dataset (REDD) has been used to verify the proposed method. REDD is related to real-world measurements recorded at low-frequency. The presented results reveal the accuracy and efficiency of the proposed method in comparison to conventional procedures of NILM

    A Microgrid Energy Management System Based on Non-Intrusive Load Monitoring via Multitask Learning

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