439 research outputs found

    Non-intrusive Zigbee power meter for load monitoring in smart buildings

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    Energy efficiency in smart buildings requires distributed sensing infrastructure to monitor the power consumption of appliances, machines and lighting sources. The analysis of current and voltage waveforms is fundamental for gathering diagnostic information about the power quality and for reducing power wastage. Moreover, it enables Non-intrusive Load Monitoring (NILM), which is the process of disaggregating a household's total electricity consumption into its contributing appliances, by analysing the voltage and current changes. In this paper, an innovative full Energy-neutral (i.e. battery free) and Non-intrusive Wireless Energy Meter (NIWEM) is presented to measure current, voltage and power factor. As key features, the NIWEM is completely non-invasive and it can self-sustain its operations by harvesting energy from the monitored load. It also features a standard (Zigbee) wireless interface for communication with the smart-building system. Experimental results have confirmed that complete energy sustainability can be achieved also with very low-power loads

    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

    Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments

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    The increased penetration of uncertain and variable renewable energy presents various resource and operational electric grid challenges. Micro-level (household and small commercial) demand-side grid flexibility could be a cost-effective strategy to integrate high penetrations of wind and solar energy, but literature and field deployments exploring the necessary information and communication technologies (ICTs) are scant. This paper presents an exploratory framework for enabling information driven grid flexibility through the Internet of Things (IoT), and a proof-of-concept wireless sensor gateway (FlexBox) to collect the necessary parameters for adequately monitoring and actuating the micro-level demand-side. In the summer of 2015, thirty sensor gateways were deployed in the city of Managua (Nicaragua) to develop a baseline for a near future small-scale demand response pilot implementation. FlexBox field data has begun shedding light on relationships between ambient temperature and load energy consumption, load and building envelope energy efficiency challenges, latency communication network challenges, and opportunities to engage existing demand-side user behavioral patterns. Information driven grid flexibility strategies present great opportunity to develop new technologies, system architectures, and implementation approaches that can easily scale across regions, incomes, and levels of development

    Integration of Legacy Appliances into Home Energy Management Systems

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    The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS

    Energy Management for Intelligent Buildings

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    PresenceSense: Zero-training Algorithm for Individual Presence Detection based on Power Monitoring

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    Non-intrusive presence detection of individuals in commercial buildings is much easier to implement than intrusive methods such as passive infrared, acoustic sensors, and camera. Individual power consumption, while providing useful feedback and motivation for energy saving, can be used as a valuable source for presence detection. We conduct pilot experiments in an office setting to collect individual presence data by ultrasonic sensors, acceleration sensors, and WiFi access points, in addition to the individual power monitoring data. PresenceSense (PS), a semi-supervised learning algorithm based on power measurement that trains itself with only unlabeled data, is proposed, analyzed and evaluated in the study. Without any labeling efforts, which are usually tedious and time consuming, PresenceSense outperforms popular models whose parameters are optimized over a large training set. The results are interpreted and potential applications of PresenceSense on other data sources are discussed. The significance of this study attaches to space security, occupancy behavior modeling, and energy saving of plug loads.Comment: BuildSys 201

    Fuzzified Single Phase Automatic Sequential Reactive Power Compensation with Minimized Switches

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    The current rapid growth in IoT technology facilitates the effortless implementation of bidirectional remote monitoring and control system implementation in homes and buildings. We have modeled an actual non-intrusive PnP sequential SVC prototype hardware and wireless FLC automation software design on a real single phase home appliances system as load modeling. In addition, we have also designed a novel Unidirectional MOSFET Switched Capasitor model (UniMosSC) which enables us to reduce the hardware cost and increase the life span of SVC due it uses minimum switching devices. The system we have designed is able to correct the power factor at the root of the problem at each appliance. Due to complexity of appliance clustering and overlapping clusters, we implemented fuzziness in the system for more reliability in computations. The system could be used in homes or buildings resulting in electricity bill reduction, saving dollars and cents
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