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

Evaluating semi-automatic annotation of domestic energy consumption as a memory aid

Frequent feedback about energy consumption can help conservation, one of the current global challenges. Such feedback is most helpful if users can relate it to their own day-to-day activities. In earlier work we showed that manual annotation of domestic energy consumption logs aids users to make such connection and discover patterns they were not aware of. In this poster we report how we augmented manual annotation with machine learning classification techniques. We propose the design of a lab study to evaluate the system, extending methods used to evaluate context aware memory aids, and we present the results of a pilot with 5 participants

Linking recorded data with emotive and adaptive computing in an eHealth environment

Telecare, and particularly lifestyle monitoring, currently relies on the ability to detect and respond to changes in individual behaviour using data derived from sensors around the home. This means that a significant aspect of behaviour, that of an individuals emotional state, is not accounted for in reaching a conclusion as to the form of response required. The linked concepts of emotive and adaptive computing offer an opportunity to include information about emotional state and the paper considers how current developments in this area have the potential to be integrated within telecare and other areas of eHealth. In doing so, it looks at the development of and current state of the art of both emotive and adaptive computing, including its conceptual background, and places them into an overall eHealth context for application and development

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Load Hiding of Household's Power Demand

With the development and introduction of smart metering, the energy information for costumers will change from infrequent manual meter readings to fine-grained energy consumption data. On the one hand these fine-grained measurements will lead to an improvement in costumers' energy habits, but on the other hand the fined-grained data produces information about a household and also households' inhabitants, which are the basis for many future privacy issues. To ensure household privacy and smart meter information owned by the household inhabitants, load hiding techniques were introduced to obfuscate the load demand visible at the household energy meter. In this work, a state-of-the-art battery-based load hiding (BLH) technique, which uses a controllable battery to disguise the power consumption and a novel load hiding technique called load-based load hiding (LLH) are presented. An LLH system uses an controllable household appliance to obfuscate the household's power demand. We evaluate and compare both load hiding techniques on real household data and show that both techniques can strengthen household privacy but only LLH can increase appliance level privacy

Smart kitchen for Ambient Assisted Living

El envejecimiento de la población es una realidad en todos los países desarrollados. Las predicciones de crecimiento de esta población son alarmantes, planteando un reto para los servicios sociales y sanitarios. Las personas ancianas padecen diversas discapacidades que se van acentuando con la edad, siendo más propensas a sufrir accidentes domésticos, presentando problemas para realizar tareas cotidianas, etc. Esta situación conlleva a una pérdida paulatina de capacidades que en muchas ocasiones acaba con la vida autónoma de la persona. En este contexto, las Tecnologías de la Información y Comunicación (TIC) aplicadas al entorno doméstico pueden jugar un papel importante, permitiendo que las personas ancianas vivan más tiempo, de forma independiente en su propio hogar, presentando, por tanto, una alternativa a la hospitalización o institucionalización de las mismas. Este trabajo da un paso más en este sentido, presentando el diseño y desarrollo de un Ambiente Inteligente en la cocina, que ayuda a las personas ancianas y/o con discapacidad a desempeñar sus actividades de la vida diaria de una forma más fácil y sencilla. Esta tesis realiza sus principales aportaciones en dos campos: El metodológico y el tecnológico. Por un lado se presenta una metodología sistemática para extraer necesidades de colectivos específicos a fin de mejorar la información disponible por el equipo de diseño del producto, servicio o sistema. Esta metodología se basa en el estudio de la interacción Hombre-Máquina en base a los paradigmas y modelos existentes y el modelado y descripción de las capacidades del usuario en la misma utilizado el lenguaje estandarizado propuesto en la Clasificación Internacional del Funcionamiento, de la Discapacidad y de la Salud (CIF). Adicionalmente, se plantea el problema de la evaluación tecnológica, diseñando la metodología de evaluación de la tecnología con la finalidad de conocer su accesibilidad, funcionalidad y usabilidad del sistema desarrollado y aplicándola a 61 usuarios y 31 profesionales de la gerontología. Desde un punto de vista técnico, se afronta el diseño de un ambiente asistido inteligente (Ambient Assisted Living, AAL) en la cocina, planteando y definiendo la arquitectura del sistema. Esta arquitectura, basada en OSGi (Open Services Gateway initiative), oferta un sistema modular, con altas capacidades de interoperabilidad y escalabilidad. Además, se diseña e implementa una red de sensores distribuida en el entorno con el fin de obtener la mayor información posible del contexto, presentando distintos algoritmos para obtener información de alto nivel: detección de caídas o localización. Todos los dispositivos presentes en el entorno han sido modelados utilizando la taxonomía propuesta en OSGi4AmI, extendiendo la misma a los electrodomésticos más habituales de la cocina. Finalmente, se presenta el diseño e implementación de la inteligencia del sistema, que en función de la información procedente del contexto y de las capacidades del usuario da soporte a las principales actividades de la vida diaria (AVD) en la cocina

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

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