1,081 research outputs found

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

    Get PDF
    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

    Understanding domestic appliance use through their linkages to common activities

    Get PDF
    Activities are a descriptive term for the common ways households spend their time. Examples include daily routines such as cooking, doing laundry, and Computing. Smart energy meter data can be used to generate time profiles of activities that are meaningful to households’ own lived experience. Activities are therefore a lens through which energy feedback to households can be made salient and understandable. This paper demonstrates how hourly time profiles of household activities can be inferred from smart energy meter data, supplemented by appliance monitors and environmental sensors. In-depth interviews and home surveys are used to identify appliances and devices used for a range of activities. These relationships between te chnologies and activities are captured in an ‘activity ontology’ that can be applied to smart meter data to make inferences on hourly time profiles of up to nine everyday activities. Results are presented from six homes participating in a UK trial of smart home technologies. The duration of activities and when they are carried out is examined within households. The time profile of domestic activities has routine characteristics but these tend to vary widely between households with different socio-demo graphic characteristics. Analysing the energy consumption associated with different activities leads to a useful means of providing activity-itemised energy feedback, and also reveals certain households to be high energy-using across a range of activities

    Energy-based decision engine for household human activity recognition

    Get PDF
    We propose a framework for energy-based human activity recognition in a household environment. We apply machine learning techniques to infer the state of household appliances from their energy consumption data and use rulebased scenarios that exploit these states to detect human activity. Our decision engine achieved a 99.1% accuracy for real-world data collected in the kitchens of two smart homes

    Uma revisão bibliométrica sobre a identificação de cargas similares em Smart Grid

    Get PDF
    In light of the need to consciously use energy resources for sustainable growth, the smart grid concept has served as a reference for the efficient use of such resources, especially the electric. However, the application of such concept in electrical installations requires the development of devices that allow the autonomous monitoring of electrical and electronic appliances in such installations. This article aims to carry out a bibliometric review on non-intrusive load monitoring methods to show the state of the art of this subject, that is, the main advances and obstacles to the development of this technology. A quantitative and qualitative analysis of the works related to the theme searched through the Web of Science database is performed. As result of such research, it is cited the observance of the relevance of the studies in this area, which is in full growth. It is also mentioned the fact that the theme is being investigated in developed countries, like Canada and the United States, as well as in developing countries, like Malaysia and India, which shows that there is a global concern in advancing in such a problem that independent of the nation's economic situation.Ante la necesidad de un uso consciente de los recursos energéticos para el crecimiento sostenible, el concepto Smart Grid ha servido de referencia para el uso eficiente de tales recursos, sobre todo el eléctrico. Sin embargo, la aplicación de tal concepto en las instalaciones eléctricas requiere el desarrollo de dispositivos que permitan el monitoreo autónomo de aparatos electroelectrónicos en tales instalaciones. Este artículo tiene como objetivo realizar una revisión bibliométrica sobre los métodos no intrusivos de monitoreo de cargas, a fin de mostrar el estado del arte de esta temática, es decir, los principales avances y obstáculos para el desarrollo de esa tecnología. Se realiza un análisis cuantitativo y cualitativo de los trabajos referentes al tema buscados vía base de datos Web of Science. Como resultado de dicha investigación, se observa la relevancia de los estudios en esa área, que está en pleno crecimiento. Se cita el hecho de que el tema está siendo investigado tanto en países desarrollados como Canadá y Estados Unidos, como en países en desarrollo, como Malasia e India, lo que muestra que existe una preocupación mundial en avanzar en tal problema que independe de la situación económica de la nación.Diante da necessidade de uso consciente dos recursos energéticos para o crescimento sustentável, o conceito Smart Grid tem servido de referência para o uso eficiente de tais recursos, sobretudo o elétrico. Entretanto, a aplicação de tal conceito nas instalações elétricas requer o desenvolvimento de dispositivos que permitam o monitoramento autônomo de aparelhos eletroeletrônicos em tais instalações. Este artigo tem como objetivo realizar uma revisão bibliométrica sobre os métodos não-intrusivo de monitoramento de cargas, a fim de mostrar o estado da arte dessa temática, isto é, os principais avanços e entraves para o desenvolvimento dessa tecnologia. É realizada uma análise quantitativa e qualitativa dos trabalhos referentes ao tema buscados via base de dados Web of Science. Como resultado de tal investigação, observa-se a relevância dos estudos nessa área, que está em pleno crescimento. Cita-se ainda o fato de o tema estar sendo investigado tanto em países desenvolvidos, como Canadá e Estados Unidos, quanto em países em desenvolvimento, como Malásia e Índia, o que mostra que existe uma preocupação mundial em se avançar em tal problema que independe da situação econômica da nação

    Electricity consumption pattern disaggregation based on user utilization factor

    Get PDF
    Non-Intrusive Appliance Load Monitoring (NIALM) technique has been studied intensively by many researchers to estimate the electricity consumption of each appliance in a monitored building. However, the method requires a detailed, secondby- second power consumption data which is commonly not available without the use of high specification energy meter. The common energy meter used in buildings can only capture low frequency data such as kWh for every thirty minutes. This thesis proposes a bottom-up approach for disaggregating kWh consumption of a building. The relationship between the load profile of a building and electricity usage pattern of the occupants were studied and analysed. From the findings, a method based on utilization factor that relates user usage pattern and kWh electricity consumption was proposed to perform load disaggregation. The method was applied on the practical kWh profile data of electricity consumption of Block P19a, Fakulti Kejuruteraan Elektrik, Universiti Teknologi Malaysia. The disaggregated kWh consumption results for air-conditioning and lighting system were validated with the actual kWh consumption recorded at the respective branch circuits of the building. Results from the analysis showed that the proposed method can be used to disaggregate energy consumption of a commercial building into air-conditioning and lighting systems. The proposed method could be extended to disaggregate the energy consumption for different areas of the building

    Measurements for non-intrusive load monitoring through machine learning approaches

    Get PDF
    The topic of non-intrusive load monitoring (NILM) has seen a significant increase in research interest over the past decade, which has led to a significant increase in the performance of these systems. Nowadays, NILM systems are used in numerous applications, in particular by energy companies that provide users with an advanced management service of different consumption. These systems are mainly based on artificial intelligence algorithms that allow the disaggregation of energy by processing the absorbed power signal over more or less long time intervals (generally from fractions of an hour up to 24 h). Less attention was paid to the search for solutions that allow non-intrusive monitoring of the load in (almost) real time, that is, systems that make it possible to determine the variations in loads in extremely short times (seconds or fractions of a second). This paper proposes possible approaches for non-intrusive load monitoring systems operating in real time, analysing them from the point of view of measurement. The measurement and post-processing techniques used are illustrated and the results discussed. In addition, the work discusses the use of the results obtained to train machine learning algorithms that allow you to convert the measurement results into useful information for the user

    An efficient scalable time-frequency method for tracking energy usage of domestic appliances using a two-step classification algorithm

    Get PDF
    Load identification is the practice of measuring electrical signals in a domestic environment in order to identify which electrical appliances are consuming power. One reason for developing a load identification system is to reduce power consumption by increasing consumers’ awareness of which appliances consume most energy. The thesis outlines the development of a load disaggregation method that measures the aggregate electrical signals of a domestic environment and extracts features to identify each power consuming appliance. A single sensor is deployed at the main incoming power point, to sample the aggregate current signal. The method senses when an appliance switches ON or OFF and uses a two-step classification algorithm to identify which appliance has caused the event. Parameters from the current in the temporal and frequency domains are used as features to de- fine each appliance. These parameters are the steady state current harmonics and the rate of change of the transient signal. Each appliance’s electrical characteristics are distinguishable using these parameters. There are three types of loads that an appliance can fall into, linear nonreactive, linear reactive or nonlinear reactive. It has been found that by identifying the load type first, and then using a second classifier to identify individual appliances within these types, the overall accuracy of the identification algorithm is improved

    Energy-Use Feedback Engineering - Technology and Information Design for Residential Users

    Get PDF
    The research presented in this study covers a first design iteration of energy feedback for residential users. This research contributes with a framework and new insights into the study of energy-use information for residential users, which exemplifies the challenges and potential of integrating information technology in this part of the energy system
    corecore