Home Energy Consumption Feedback: A User Survey

Buildings account for a relevant fraction of the energy consumed by a country, up to 20-40% of the yearly energy consumption. If only electricity is considered, the fraction is even bigger, reaching around 73% of the total electricity consumption, equally divided into residential and commercial dwellings. Building and Home Automation have a potential to profoundly impact current and future buildings' energy efficiency by informing users about their current consumption patterns, by suggesting more efficient behaviors, and by pro-actively changing/modifying user actions for reducing the associated energy wastes. In this paper we investigate the capability of an automated home to automatically, and timely, inform users about energy consumption, by harvesting opinions of residential inhabitants on energy feedback interfaces. We report here the results of an on-line survey, involving nearly a thousand participants, about feedback mechanisms suggested by the research community, with the goal of understanding what feedback is felt by home inhabitants easier to understand, more likely to be used, and more effective in promoting behavior changes. Contextually, we also collect and distill users' attitude towards in-home energy displays and their preferred locations, gathering useful insights on user-driven design of more effective in-home energy display

Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.Peer reviewe

Unobtrusive Health Monitoring in Private Spaces: The Smart Home

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in n=55 papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (n=38), time spent on activities (n=18)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (n=5). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking

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

The uncertainty of the energy demand in existing mediterranean urban blocks

The objective of the paper is to describe a stochastic model that has been developed to obtain load profiles for household electricity. For the study, several profiles have been generated in order to simulate the electrical demand of a residential building block or neighbourhood and evaluate the uncertainty of its energy use. The paper is divided in three different parts: development of the model, validation and determination of the uncertainty demand. In the first parts the basis of the model and how it works is explained. The second one represents the validation of the model, the input data and its results. The last step is focused on a statistical analysis of the electricity demand of a block of dwellings to evaluate minimum number of dwellings needed to estimate the average demand representative of the Mediterranean dwelling with different levels of accuracyPostprint (author’s final draft

Design and Implementation of Wireless Smart Home Energy Management System Using Rule-Based Controller

Most residential units still rely on conventional energy supplied by utilities despite the continuous growth of renewable energy resources, such as solar and wind energy systems in power distribution networks. Utilities often use time-of-use energy pricing, which increases the interest of energy consumers, such as those in commercial and residential buildings, in reducing their energy usage. Thus, this work demonstrates the design and implementation of a home energy management (HEM) system that can automatically control home appliances to reduce daily energy and electricity bill. The system consists of multiple smart sockets that can read the power consumption of an attached appliance and actuate its on/off commands. It also consists of several other supporting instruments that provide information to the main controller. The smart sockets and supporting instruments in the system wirelessly provide the necessary data to a central controller. Then, the system analyzes the data gathered from these devices to generate control commands that operate the devices attached to the smart sockets. Control actions rely on a developed online rule-based HEM scheme. The rules of the algorithm are designed such that the lifestyle of the user is preserved while the energy consumption and daily energy cost of the controlled appliances are reduced. Experimental results show that the central controller can effectively receive data and control multiple devices from up to 18 m away without loss of data on the basis of a scheduled user program code. Moreover, online adaptation of the HEM scheme confirms significant reductions in the total daily energy consumption and daily electricity bill of 23.5 kWh and $2.898, respectively. Therefore, the proposed HEM system can be remarkably useful for home owners with high daily energy consumption

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

Smart Kitchens for People with Cognitive Impairments: A Qualitative Study of Design Requirements

Individuals with cognitive impairments currently leverage extensive human resources during their transitions from assisted living to independent living. In Western Europe, many government-supported volunteer organizations provide sheltered living facilities; supervised environments in which people with cognitive impairments collaboratively learn daily living skills. In this paper, we describe communal cooking practices in sheltered living facilities and identify opportunities for supporting these with interactive technology to reduce volunteer workload. We conducted two contextual observations of twelve people with cognitive impairments cooking in sheltered living facilities and supplemented this data through interviews with four employees and volunteers who supervise them. Through thematic analysis, we identified four themes to inform design requirements for communal cooking activities: Work organization, community, supervision, and practicalities. Based on these, we present five design implications for assistive systems in kitchens for people with cognitive deficiencies