17 research outputs found

    Household occupancy monitoring using electricity meters

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    Occupancy monitoring (i.e. sensing whether a building or room is currently occupied) is required by many building au-tomation systems. An automatic heating system may, for ex-ample, use occupancy data to regulate the indoor temperature. Occupancy data is often obtained through dedicated hardware such as passive infrared sensors and magnetic reed switches. In this paper, we derive occupancy information from elec-tric load curves measured by off-the-shelf smart electricity meters. Using the publicly available ECO dataset, we show that supervised machine learning algorithms can extract occu-pancy information with an accuracy between 83 % and 94%. To this end we use a comprehensive feature set containing 35 features. Thereby we found that the inclusion of features that capture changes in the activation state of appliances provides the best occupancy detection accuracy

    Energy Data Analytics for Smart Meter Data

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    The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

    Waldo: Batteryless Occupancy Monitoring with Reflected Ambient Light

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    Reliable and accurate room-level occupancy-tracking systems can enable many new advances in sensors and applications of modern smart buildings. This allows buildings to be more capable of adapting to the needs of their occupants in their day-to-day activities and better optimize certain resources, such as power and air conditioning, to do so. Unfortunately, existing occupancy-tracking systems are plagued by large size, high energy consumption, and, unsurprisingly, short battery lifetimes. In this paper, we present Waldo, a batteryless, room-level occupancy monitoring sensor that harvests energy from indoor ambient light reflections, and uses changes in these reflections to detect when people enter and exit a room. Waldo is mountable at the top of a doorframe, allowing for detection of a person and the direction they are traveling at the entry and exit point of a room. We evaluated the Waldo sensor in an office-style setting under mixed lighting conditions (natural and artificial) on both sides of the doorway with subjects exhibiting varying physical characteristics such as height, hair color, gait, and clothing. 651 number of controlled experiments were ran on 6 doorways with 12 individuals and achieved a total detection accuracy of 97.38%. Further, it judged the direction of movement correctly with an accuracy of 95.42%. This paper also evaluates and discusses various practical factors that can impact the performance of the current system in actual deployments. This work demonstrates that ambient light reflections provide both a promising low-cost, long-term sustainable option for monitoring how people use buildings and an exciting new research direction for batteryless computing

    Elektromos készülékek statisztikai paramétereinek becslése időpont alapján = Estimation of Statistical Parameters of Electric Appliances based on Time of Day Information

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    A közelmúltban kifejlesztésre került egy a Smart Gridbe illeszkedő fogyasztásengedélyezési eljárás, amely eljárás a fogyasztói oldal befolyásolásának problémáját (Demand Side Management) az egyes fogyasztók statisztikai leírói alapján oldja meg. A fogyasztói oldal befolyásolásával a villamosenergia kereslet a megtermelt mennyiséghez igazítható, lehetővé téve a megújuló energiatermelők nagyobb mértékű kihasználását. Az új eljárás érzékeny a statisztikai leírók pontosságára, amely leírók nem stacionáriusak, sőt további paraméterektől is függenek, például jelenlét, hőmérséklet, stb. Cikkünkben megvizsgáljuk, hogy radiális bázisfüggvényes neurális hálózattal milyen hatékonysággal becsülhetők meg idővariáns statisztikai leírók. A módszer hatékonyságát nyilvánosan elérhető villamos fogyasztási adatbázis felhasználásával értékeltük numerikusan

    Multi-User Low Intrusive Occupancy Detection

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    Smart spaces are those that are aware of their state and can act accordingly. Among the central elements of such a state is the presence of humans and their number. For a smart office building, such information can be used for saving energy and safety purposes. While acquiring presence information is crucial, using sensing techniques that are highly intrusive, such as cameras, is often not acceptable for the building occupants. In this paper, we illustrate a proposal for occupancy detection which is low intrusive; it is based on equipment typically available in modern offices such as room-level power-metering and an app running on workers’ mobile phones. For power metering, we collect the aggregated power consumption and disaggregate the load of each device. For the mobile phone, we use the Received Signal Strength (RSS) of BLE (Bluetooth Low Energy) nodes deployed around workspaces to localize the phone in a room. We test the system in our offices. The experiments show that sensor fusion of the two sensing modalities gives 87–90% accuracy, demonstrating the effectiveness of the proposed approach

    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

    Power estimation of multiple two-state loads using a probabilistic non-intrusive approach

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    This paper investigates a non-intrusive approach of retrieving electric space heater (ESH) power profiles from a residential aggregated signal. In cold-climate regions with heating appliances controlled by electronic thermostats, an accurate non-intrusive recognition of power profiles is a challenging task. Accordingly, a robust disaggregation approach based on the difference factorial hidden Markov model (DFHMM) and the Kronecker operation is contributed. The proposed method aims to uncover the underlying stochastic tow-state models of ESHs using their common prior knowledge. The major advantage of the developed load-monitoring architecture consists of modeling simplicity and inference as well as load-detection efficacy in the presence of perturbations from other unknown loads. The experimental results prove the effectiveness of the method in manipulating the challenging case of multiple two-state loads with a high event overlapping probability

    Non-intrusive load monitoring of household devices using a hybrid deep learning model through convex hull-based data selection

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    The availability of smart meters and IoT technology has opened new opportunities, ranging from monitoring electrical energy to extracting various types of information related to household occupancy, and with the frequency of usage of different appliances. Non-intrusive load monitoring (NILM) allows users to disaggregate the usage of each device in the house using the total aggregated power signals collected from a smart meter that is typically installed in the household. It enables the monitoring of domestic appliance use without the need to install individual sensors for each device, thus minimizing electrical system complexities and associated costs. This paper proposes an NILM framework based on low frequency power data using a convex hull data selection approach and hybrid deep learning architecture. It employs a sliding window of aggregated active and reactive powers sampled at 1 Hz. A randomized approximation convex hull data selection approach performs the selection of the most informative vertices of the real convex hull. The hybrid deep learning architecture is composed of two models: a classification model based on a convolutional neural network trained with a regression model based on a bidirectional long-term memory neural network. The results obtained on the test dataset demonstrate the effectiveness of the proposed approach, achieving F1 values ranging from 0.95 to 0.99 for the four devices considered and estimation accuracy values between 0.88 and 0.98. These results compare favorably with the performance of existing approaches.This research was funded by Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020, grant numbers 39578/2018 and 72581/2020. Antonio Ruano also acknowledges the support of Fundação para a Ciência e Tecnologia, grant UID/EMS/50022/2020, through IDMEC under LAETAinfo:eu-repo/semantics/publishedVersio

    A Framework for Analysis and Expansion of Public Charging Infrastructure under Fast Penetration of Electric Vehicles

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    The improvement commercial competitiveness of private electric vehicles supported by the European policy for the decarbonisation of transport and with the consumers awareness-raising about CO2 emissions and climate change, are driving the increase of electric vehicles on the roads. Therefore, public charging networks are facing the challenge of supply electricity to a fast increasing number of electric cars. The objective of this paper is to establish an assessment framework for analysis and monitor of existing charging networks. The developed methodology comprises modelling the charging infrastructure electricity profile, analysing the data by using machine learning models such as functional k-means clustering and defining a novel congestion metric. The described framework has been tested against Irish public charging network historical datasets. The analyses reveal a lack of reliability of the communication network infrastructure, frequent congestion events for commercial and shopping areas in specific clusters of charge points and the presence of power peaks caused by the high number of simultaneous charging events. Several recommendations for future network expansion have been highlighted
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