Intelligent Energy Optimization for User Intelligible Goals in Smart Home Environments

Intelligent management of energy consumption is one of the key issues for future energy distribution systems, smart buildings, and consumer appliances. The problem can be tackled both from the point of view of the utility provider, with the intelligence embedded in the smart grid, or from the point of view of the consumer, thanks to suitable local energy management systems (EMS). Conserving energy, however, should respect the user requirements regarding the desired state of the environment, therefore an EMS should constantly and intelligently find the balance between user requirements and energy saving. The paper proposes a solution to this problem, based on explicit high-level modeling of user intentions and automatic control of device states through the solution and optimization of a constrained Boolean satisfiability problem. The proposed approach has been integrated into a smart environment framework, and promising preliminary results are reporte

Designing for User Confidence in Intelligent Environments

Intelligent environments aim at supporting and assisting users in their daily activities. Their reliability, i.e., the capability of correctly accomplishing the intended tasks and of limiting or avoiding damage in case of malfunctions, is essential as for any user-facing technology. One aspect of reliability, often neglected, is guaranteeing the consistency between system operation and user expectations, so that users may build confidence over the correct behavior of the system and its reaction to their actions. The paper will review the literature concerning methodologies and tools that directly involve users and have been specifically applied or adopted for intelligent environments, throughout the entire design flow – from requirements gathering to interface design. The paper will then propose, building on top of the previous analysis, a set of guidelines that system designers should follow to ensure user confidence in their intelligent environments

Real-Time Monitoring of High-Level States in Smart Environments

Modern smart environments are equipped with a multitude of devices and sensors aimed at intelligent services. The presence of these diverse devices has raised a major problem of managing complex environments. A rising solution to the problem is the modeling of user goals and intentions, and then interacting with the respective smart environments using user defined goals. Generally, the solution advocates that the user goal(s) can be represented by combining devices (smart appliances and sensor/actuators) in particular states. `Domotic Effects' is a high level modeling approach, which provides Ambient Intelligence (AmI) designers and integrators with a high level abstract layer that enables the definition of user goals in a smart environment, in a declarative way, which can be used to design and develop intelligent applications. This paper describes an approach for the automatic evaluation of domotic effects combined through Boolean expressions, that can provide efficient and intelligent monitoring of the domotic structure of the environment. ``Effects Evaluation'' addresses the problem of finding the new values of all the domotic effects defined for the environment when one or more devices change their state or one or more sensor value is recorded in the environment, hence determining a new overall state of the environment. The paper also presents an architecture to implement the evaluation of domotic effects. Results obtained from carried out experiments prove the feasibility of the approach and highlight responsiveness of the implemented effect evaluation

Comfort-Oriented Metamorphic House (COMETAE)

Publications Internes de l'IRISA ISSN : 2102-6327This proposal aims at challenging the existing design paradigm of residential building architecture with insights from pervasive computing, robotics, human-computer interaction and cognitive ergonomics. ICT and architecture will be holistically integrated in order to realize a radical advancement leading to metamorphic houses. Supported by ICT, domestic environments will self-adapt to the ongoing activities of inhabitants, to increase the comfort of living and optimize the use of space and energy. The same physical space will be transformed for diff erent uses, giving inhabitants the illusion of living in a bigger, more adapted and more comfortable place. The traditional tradeoff between comfort and energy conservation will also be revisited, thanks to an optimal exploitation of natural light, heat and ventilation. The realization of the targeted breakthrough will be achieved through cross-fertilization between involved disciplines and by the support of a panel of final users, architects and building engineers in the design, development and evaluation phases. COMETAE will introduce a novel approach to smart spaces research, where the space is itself an actuator of the system. Indoor environment, space and energy use will be optimized with respect to environmental factors, occupants' activities and life cycle, by orchestrating adaptive robotic building components, while ensuring occupants' safety. New coupling between inhabitants and their environment will be enabled by combining human-computer interaction and space recon guration, considering beauty of interaction and multi-user scenarios. This proposal opens the way towards a radically new use of ICT, needed to address future limitations of space and energy in the domestic environment, imposed by the ongoing and future evolutions of technology, environmental questions and socio-demographic factors

Paying Attention to the Man behind the Curtain

In the push to develop smart energy systems, designers have increasingly focused on systems that measure and predict user behavior to effect optimal energy consumption. While such focus is an important component in these systems' success, designers have paid substantially less attention to the people on the other side of the energy system loop-the supervisors of power generation processes. Smart energy systems that leverage pervasive computing could add to these supervisory control operators' workload. They'll have to predict possible power plant load and production changes caused by environmental and plant events, as well as dynamic system adaptation in response to consumer behaviors. Contrary to many assumptions, inserting more automation, including distributed sensors and algorithms to postprocess data, won't necessarily reduce operators' workload or improve system performance

Supporting User Understanding and Engagement in Designing Intelligent Systems for the Home.

With advances in computing, networking and sensing technology, our everyday objects have become more automated, connected, and intelligent. This dissertation aims to inform the design and implementation of future intelligent systems and devices. To do so, this dissertation presents three studies that investigated user interaction with and experience of intelligent systems. In particular, we look at intelligent technologies that employ sensing technology and machine learning algorithm to perceive and respond to user behavior, and that support energy savings in the home. We first investigated how people understand and use an intelligent thermostat in their everyday homes to identify problems and challenges that users encounter. Subsequently, we examined the opportunities and challenges for intelligent systems that aimed to save energy, by comparing how people’s interaction changed between conventional and smart thermostats as well as how interaction with smart thermostats changed over time. These two qualitative studies led us to the third study. In the final study, we evaluated a smart thermostat that offered a new approach to the management of thermostat schedule in a field deployment, exploring effective ways to define roles for intelligent systems and their users in achieving their mutual goals of energy savings. Based on findings from these studies, this dissertation argues that supporting user understanding and user control of intelligent systems for the home is critical allowing users to intervene effectively when the system does not work as desired. In addition, sustaining user engagement with the system over time is essential for the system to obtain necessary user input and feedback that help improve the system performance and achieve user goals. Informed by findings and insights from the studies, we identify design challenges and strategies in designing end-user interaction with intelligent technologies for the home: making system behaviors intuitive and intelligible; maintaining long-term, easy user engagement over time; and balancing interplay between user control and system autonomy to better achieve their mutual goals.PhDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133318/1/rayang_1.pd

Through the clouds : urban analytics for smart cities

Data has been collected since mankind, but in the recent years the technical innovations enable us to collect exponentially growing amounts of data through the use of sensors, smart devices and other sources. In her lecture Nanda will explore the role of Big Data in urban environments. She will give an introduction to the world of Big Data and Smart Cities, and an assessment of the role that data analytics plays in the current state of the digital transformation in our cities. Examples are given in the field of energy and mobility

Internet of things (IoT) based adaptive energy management system for smart homes

PhD ThesisInternet of things enhances the flexibility of measurements under different environments, the development of advanced wireless sensors and communication networks on the smart grid infrastructure would be essential for energy efficiency systems. It makes deployment of a smart home concept easy and realistic. The smart home concept allows residents to control, monitor and manage their energy consumption with minimal wastage. The scheduling of energy usage enables forecasting techniques to be essential for smart homes. This thesis presents a self-learning home management system based on machine learning techniques and energy management system for smart homes. Home energy management system, demand side management system, supply side management system, and power notification system are the major components of the proposed self-learning home management system. The proposed system has various functions including price forecasting, price clustering, power forecasting alert, power consumption alert, and smart energy theft system to enhance the capabilities of the self-learning home management system. These functions were developed and implemented through the use of computational and machine learning technologies. In order to validate the proposed system, real-time power consumption data were collected from a Singapore smart home and a realistic experimental case study was carried out. The case study had proven that the developed system performing well and increased energy awareness to the residents. This proposed system also showcases its customizable ability according to different types of environments as compared to traditional smart home models. Forecasting systems for the electricity market generation have become one of the foremost research topics in the power industry. It is essential to have a forecasting system that can accurately predict electricity generation for planning and operation in the electricity market. This thesis also proposed a novel system called multi prediction system and it is developed based on long short term memory and gated recurrent unit models. This proposed system is able to predict the electricity market generation with high accuracy. Multi Prediction System is based on four stages which include a data collecting and pre-processing module, a multi-input feature model, multi forecast model and mean absolute percentage error. The data collecting and pre-processing module preprocess the real-time data using a window method. Multi-input feature model uses single input feeding method, double input feeding method and multiple feeding method for features input to the multi forecast model. Multi forecast model integrates long short term memory and gated recurrent unit variations such as regression model, regression with time steps model, memory between batches model and stacked model to predict the future generation of electricity. The mean absolute percentage error calculation was utilized to evaluate the accuracy of the prediction. The proposed system achieved high accuracy results to demonstrate its performance