5,474 research outputs found
Machine learning for smart building applications: Review and taxonomy
© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field
Smart workplaces: a system proposal for stress management
Over the past last decades of contemporary society, workplaces
have become the primary source of many health issues, leading
to mental problems such as stress, depression, and anxiety.
Among the others, environmental aspects have shown to be the
causes of stress, illness, and lack of productivity. With the arrival
of new technologies, especially in the smart workplaces field,
most studies have focused on investigating the building energy
efficiency models and human thermal comfort. However, little has
been applied to occupants’ stress recognition and well-being
overall. Due to this fact, this present study aims to propose a
stress management solution for an interactive design system that
allows the adapting of comfortable environmental conditions
according to the user preferences by measuring in real-time the
environmental and biological characteristics, thereby helping to
prevent stress, as well as to enable users to cope stress when
being stressed. The secondary objective will focus on evaluating
one part of the system: the mobile application. The proposed
system uses several usability methods to identify users’ needs,
behavior, and expectations from the user-centered design
approach. Applied methods, such as User Research, Card
Sorting, and Expert Review, allowed us to evaluate the design
system according to Heuristics Analysis, resulting in improved
usability of interfaces and experience. The study presents the
research results, the design interface, and usability tests.
According to the User Research results, temperature and noise
are the most common environmental stressors among the users
causing stress and uncomfortable conditions to work in, and the
preference for physical activities over the digital solutions for
coping with stress. Additionally, the System Usability Scale (SUS)
results identified that the system’s usability was measured as
“excellent” and “acceptable” with a final score of 88 points out of
the 100. It is expected that these conclusions can contribute to
future investigations in the smart workplaces study field and their
interaction with the people placed there.Nas últimas décadas da sociedade contemporânea, o local de
trabalho tem se tornado principal fonte de muitos problemas de
saĂşde mental, como o stress, depressĂŁo e ansiedade. Os aspetos
ambientais têm se revelado como as causas de stress, doenças,
falta de produtividade, entre outros. Atualmente, com a chegada de
novas tecnologias, principalmente na área de locais de trabalho
inteligentes, a maioria dos estudos tem se concentrado na
investigação de modelos de eficiĂŞncia energĂ©tica de edifĂcios e
conforto térmico humano. No entanto, pouco foi aplicado ao
reconhecimento do stress dos ocupantes e ao bem-estar geral das
pessoas. Diante disso, o objetivo principal Ă© propor um sistema de
design de gestĂŁo do stress para um sistema de design interativo que
permita adaptar as condições ambientais de acordo com as
preferĂŞncias de utilizador, medindo em tempo real as caracterĂsticas
ambientais e biológicas, auxiliando assim na prevenção de stress,
bem como ajuda os utilizadores a lidar com o stress quando estĂŁo
sob o mesmo. O segundo objetivo Ă© desenhar e avaliar uma parte
do projeto — o protótipo da aplicação móvel através da realização
de testes de usabilidade. O sistema proposto resulta da abordagem
de design centrado no utilizador, utilizando diversos métodos de
usabilidade para identificar as necessidades, comportamentos e as
expectativas dos utilizadores. MĂ©todos aplicados, como Pesquisa de
Usuário, Card Sorting e Revisão de Especialistas, permitiram avaliar
o sistema de design de acordo com a análise heurĂstica, resultando
numa melhoria na usabilidade das interfaces e experiĂŞncia. O
estudo apresenta os resultados da pesquisa, a interface do design e
os testes de usabilidade. De acordo com os resultados de User
Research, a temperatura e o ruĂdo sĂŁo os stressores ambientais
mais comuns entre os utilizadores, causando stresse e condições
menos favoráveis para trabalhar, igualmente existe uma preferência
por atividades fĂsicas sobre as soluções digitais na gestĂŁo do
stresse. Adicionalmente, os resultados de System Usability Scale
(SUS) identificaram a usabilidade do sistema de design como
“excelente” e “aceitável” com pontuação final de 88 pontos em 100.
É esperado que essas conclusões possam contribuir para futuras
investigações no campo de estudo dos smart workplaces e sua
interação com os utilizadores
A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
Recommender systems have significantly developed in recent years in parallel
with the witnessed advancements in both internet of things (IoT) and artificial
intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI,
multiple forms of data are incorporated in these systems, e.g. social,
implicit, local and personal information, which can help in improving
recommender systems' performance and widen their applicability to traverse
different disciplines. On the other side, energy efficiency in the building
sector is becoming a hot research topic, in which recommender systems play a
major role by promoting energy saving behavior and reducing carbon emissions.
However, the deployment of the recommendation frameworks in buildings still
needs more investigations to identify the current challenges and issues, where
their solutions are the keys to enable the pervasiveness of research findings,
and therefore, ensure a large-scale adoption of this technology. Accordingly,
this paper presents, to the best of the authors' knowledge, the first timely
and comprehensive reference for energy-efficiency recommendation systems
through (i) surveying existing recommender systems for energy saving in
buildings; (ii) discussing their evolution; (iii) providing an original
taxonomy of these systems based on specified criteria, including the nature of
the recommender engine, its objective, computing platforms, evaluation metrics
and incentive measures; and (iv) conducting an in-depth, critical analysis to
identify their limitations and unsolved issues. The derived challenges and
areas of future implementation could effectively guide the energy research
community to improve the energy-efficiency in buildings and reduce the cost of
developed recommender systems-based solutions.Comment: 35 pages, 11 figures, 1 tabl
A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings
Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio
Evaluation and improvement of energy flexibility and performance of building heating, ventilation, and air-conditioning systems
The foreseen reduction of available fossil fuels, the continued increase in global energy demand, and the irrefutable evidence of climate change, along with the implementation of a global commitment to achieve a net-zero emissions target, have greatly sharpened commercial interest in using renewable energy resources (RER). However, the high penetration of RER-based stochastic power generation systems has resulted in a significant requirement for increased flexibility on the demand side that can allow buildings to adapt to increasingly dynamic energy supply conditions to support power grid operation and optimization. Failure to adapt may carry serious electrical blackouts and can compromise the safety of the supply side.
The building sector accounts for a substantial amount of global energy usage and offers great opportunities for energy flexibility. Building energy flexibility is an important and emerging concept in the modern energy landscape, which can support the sustainable transition of the power sector. Building heating, ventilation, and air-conditioning (HVAC) systems are one of the leading energy consumers in buildings, which can be used as a key flexible source. The HVAC systems with integrated thermal energy storage (TES) can further enhance building energy flexibility.
This thesis contributes to the evolving field of demand flexibility and introduces methodologies to evaluate and improve energy flexibility and performance of building HVAC systems
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