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

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

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Non-Invasive Ambient Intelligence in Real Life: Dealing with Noisy Patterns to Help Older People

This paper aims to contribute to the field of ambient intelligence from the perspective of real environments, where noise levels in datasets are significant, by showing how machine learning techniques can contribute to the knowledge creation, by promoting software sensors. The created knowledge can be actionable to develop features helping to deal with problems related to minimally labelled datasets. A case study is presented and analysed, looking to infer high-level rules, which can help to anticipate abnormal activities, and potential benefits of the integration of these technologies are discussed in this context. The contribution also aims to analyse the usage of the models for the transfer of knowledge when different sensors with different settings contribute to the noise levels. Finally, based on the authors’ experience, a framework proposal for creating valuable and aggregated knowledge is depicted.This research was partially funded by Fundación Tecnalia Research & Innovation, and J.O.-M. also wants to recognise the support obtained from the EU RFCS program through project number 793505 ‘4.0 Lean system integrating workers and processes (WISEST)’ and from the grant PRX18/00036 given by the Spanish Secretaría de Estado de Universidades, Investigación, Desarrollo e Innovación del Ministerio de Ciencia, Innovación y Universidades

Integration of Legacy Appliances into Home Energy Management Systems

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

Non-intrusive load monitoring techniques for activity of daily living recognition

Esta tesis nace con la motivación de afrontar dos grandes problemas de nuestra era: la falta de recursos energéticos y el envejecimiento de la población. Respecto al primer problema, nace en la primera década de este siglo el concepto de Smart Grids con el objetivo de alcanzar la eficiencia energética. Numerosos países comienzan a realizar despliegues masivos de contadores inteligentes ("Smart Meters"), lo que despierta el interés de investigadores que comienzan a desarrollar nuevas técnicas para predecir la demanda. Así, los sistemas NILM (Non-Intrusive Load Monitoring) tratan de predecir el consumo individual de los dispositivos conectados a partir de un único sensor: el contador inteligente. Por otra parte, los grandes avances en la medicina moderna han permitido que nuestra esperanza de vida aumente considerablemente. No obstante, esta longevidad, junto con la baja fertilidad en los países desarrollados, tiene un efecto secundario: el envejecimiento de la población. Unos de los grandes avances es la incorporación de la tecnología en la vida cotidiana, lo que ayuda a los más mayores a llevar una vida independiente. El despliegue de una red de sensores dentro de la vivienda permite su monitorización y asistencia en las tareas cotidianas. Sin embargo, son intrusivos, no escalables y, en algunas ocasiones, de alto coste, por lo que no están preparados para hacer frente al incremento de la demanda de esta comunidad. Esta tesis doctoral nace de la motivación de afrontar estos problemas y tiene dos objetivos principales: lograr un modelo de monitorización sostenible para personas mayores y, a su vez, dar un valor añadido a los sistemas NILM que despierte el interés del usuario final. Con este objetivo, se presentan nuevas técnicas de monitorización basadas en NILM, aunando lo mejor de ambos campos. Esto supone un ahorro considerable de recursos en la monitorización, ya que únicamente se necesita un sensor: el contador inteligente; lo cual da escalabilidad a estos sistemas. Las contribuciones de esta tesis se dividen en dos bloques principales. En el primero se proponen nuevas técnicas NILM optimizadas para la detección de la actividad humana. Así, se desarrolla una propuesta basada en detección de eventos (conexiones de dispositivos) en tiempo real y su clasificación a un dispositivo. Con el objetivo de que pueda integrarse en contadores inteligentes. Cabe destacar que el clasificador se basa en modelos generalizados de dispositivos y no necesita conocimiento específico de la vivienda. El segundo bloque presenta tres nuevas técnicas de monitorización de personas mayores basadas en NILM. El objetivo es proporcionar una monitorización básica pero eficiente y altamente escalable, ahorrando en recursos. Los procesos Cox, log Gaussian Cox Processes (LGCP), monitorizan un único dispositivo si la rutina está estrechamente ligada a este. Así, se propone un sistema de alarmas si se detectan cambios en el comportamiento. LGCP tiene la ventaja de poder modelar periodicidades e incertidumbres propias del comportamiento humano. Cuando la rutina no depende de un único dispositivo, se proponen dos técnicas: una basada en gaussianas mixtas, Gaussian Mixture Models (GMM); y la otra basada en la Teoría de la Evidencia de Dempster-Shafer (DST). Ambas monitorizan y detectan deterioros en la actividad, causados por enfermedades como la demencia y el alzhéimer. Únicamente DST usa incertidumbres que simulan mejor el comportamiento humano y, por tanto, permite alarmas en caso de un repentino desvío. Finalmente, todas las propuestas han sido validadas mediante la evaluación de métricas y la obtención de resultados experimentales. Para ello, se han usado medidas de escenarios reales que han sido recopiladas en bases de datos. Los resultados obtenidos han sido satisfactorios, demostrando que este tipo de monitorización es posible y muy beneficioso para nuestra sociedad. Además, se ha dado a lugar nuevas propuestas que serán desarrolladas en el futuro. Códigos UNESCO: 120320 - sistemas de control medico, 332201 – distribución de la energía, 120701 – análisis de actividades, 120304 – inteligencia artificial, 120807 – plausibilidad, 221402 – patrones

Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201

Measuring the energy intensity of domestic activities from smart meter data

Household electricity consumption can be broken down to appliance end-use through a variety of methods such as modelling, sub-metering, load disaggregation or non-intrusive appliance load monitoring (NILM). We advance and complement this important field of energy research through an innovative methodology that characterises the energy consumption of domestic life by making the linkages between appliance end-use and activities through an ontology built from qualitative data about the household and NILM data. We use activities as a descriptive term for the common ways households spend their time at home. These activities, such as cooking or laundering, are meaningful to households’ own lived experience. Thus, besides strictly technical algorithmic approaches for processing quantitative smart meter data, we also draw on social science time use approaches and interview and ethnography data. Our method disaggregates a households total electricity load down to appliance level and provides the start time, duration, and total electricity consumption for each occurrence of appliance usage. We then make inferences about activities occurring in the home by combining these disaggregated data with an ontology that formally specifies the relationships between electricity-using appliances and activities. We also propose two novel standardised metrics to enable easy quantifiable comparison within and across households of the energy intensity and routine of activities of interest. Finally, we demonstrate our results over a sample of ten households with an in-depth analysis of which activities can be inferred with the qualitative and quantitative data available for each household at any time, and the level of accuracy with which each activity can be inferred, unique to each household. This work has important applications from providing meaningful energy feedback to households to comparing the energy efficiency of households’ daily activities, and exploring the potential to shift the timing of activities for demand management

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems