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Publication
Gestão inteligente e distribuída de comunidades de cidadãos
| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 5.07 MB | Adobe PDF |
Authors
Abstract(s)
A utilização e integração de modelos inteligentes nos edifícios pode transformar as experiências
dos utilizadores dentro do edifício, proporcionando a otimização dos espaços e formas
eficientes de utilizar e interagir com os recursos do edifício. A utilização de soluções inteligentes
traz alguns desafios que devem ser estudados, como a heterogeneidade entre os recursos e a
necessidade de adaptar os edifícios já existentes ao conceito de edifícios inteligentes.
Embora os edifícios inteligentes possam revolucionar a forma como as pessoas utilizam e
interagem com os espaços, o grupo de edifícios, ao criar comunidades, traz novas
oportunidades para permitir que os membros interligados atinjam objetivos comuns,
modelando papéis cooperativos, colaborativos e, por vezes, competitivos. Esta nova dinâmica
em que os sistemas orgânicos podem comunicar e interagir também levanta desafios quanto à
modelação dos utilizadores, às suas preferências e à existência de infraestruturas comuns para
permitir a implementação de modelos inteligentes ao nível da comunidade, edifício e utilizador.
Esta dissertação tem como objetivo conceber, implementar, testar e validar uma infraestrutura
baseada em containers, intitulada Caravels, que combina os conceitos de comunidades
inteligentes e edifícios inteligentes para desenvolver uma solução sensível ao contexto que
considera diferentes utilizadores e edifícios. A solução concebida emprega uma arquitetura
distribuída para a gestão de comunidades inteligentes de cidadãos, onde cada membro opera
como uma entidade autónoma, enquanto permanece interligado através de uma infraestrutura
partilhada. A arquitetura permite serviços tanto a nível local como comunitário, sendo que um
membro pode integrar serviços individuais, escolhidos especificamente para esse utilizador, ao
mesmo tempo que contribui e beneficia de otimizações a nível comunitário.
Central ao projeto está a modelação das preferências do utilizador em ambientes complexos,
dinâmicos e multiutilizador. A dissertação explora os desafios psicológicos e cognitivos da
representação de preferências, reconhecendo que os utilizadores têm dificuldades em articular
ou priorizar as suas próprias preferências. Os modelos propostos podem adaptar-se ao longo
do tempo, incorporando feedback e dados comportamentais para apoiar a tomada de decisões
proativas e conscientes do contexto. As técnicas de inteligência artificial, incluindo a
aprendizagem supervisionada, não supervisionada e por reforço, estão integradas em todo o
sistema para permitir a análise preditiva, a otimização e o controlo autónomo.
Para validar a arquitetura e as metodologias propostas, foram conduzidos vários estudos de
caso em cenários realistas, refletindo as diferentes necessidades dos utilizadores, procura de
energia e recursos distribuídos. Os resultados demonstram que o sistema pode modelar o
comportamento do utilizador, apoiar a cooperação a nível comunitário e melhorar a eficiência
e a inteligência geral do edifício inteligente. Os resultados desta dissertação contribuíram para
seis publicações científicas, incluindo uma revista com um fator de impacto de 6,6.
The use and integration of intelligent models in buildings can transform the users’ experiences inside the building, being able to provide optimization of spaces and efficient ways to use and interact with the building resources. The use of these intelligent solutions brings some challenges that must be studied, such as the heterogeneity among resources and the need of retrofitting to update the already existing buildings to the concept of intelligent buildings. Although intelligent buildings can revolutionize how people use and interact with spaces, the collective of buildings, creating communities, brings new opportunities to enable interconnected members to pursue common goals, modeling cooperative, collaborative, and, sometimes, competitive roles. This new dynamic where organic systems can communicate and interact also raises challenges regarding the modeling of users, users’ preferences, and the existence of common infrastructures to enable the implementation of intelligent models at community, building, and user levels. This dissertation aims to conceive, implement, test, and validate a container-based infrastructure, entitled Caravels, which combines the concepts of intelligent communities and intelligent buildings to develop a context-aware solution that considers different users and different buildings. The conceived solution, Caravels, employs a distributed architecture for the intelligent management of citizen intelligent communities, where each member operates as an autonomous and intelligent entity, while remaining interconnected through a shared infrastructure. The architecture enables both local and community-level services, meaning that a member can integrate individual services, specifically chosen for that user, while still contributing to and benefiting from community-level optimizations. Central to this work is the modeling of user preferences in complex, dynamic, and multi-user environments. The dissertation explores the psychological and cognitive challenges of preference representation, recognizing that users often struggle to articulate or prioritize their own preferences. The proposed models can adapt over time, incorporating feedback and behavioral data to support proactive and context-aware decision-making. Artificial intelligence techniques, including supervised, unsupervised, and reinforcement learning, are integrated across the system to enable predictive analytics, optimization, and autonomous control. To validate the proposed architecture and methodologies, several case studies were conducted in realistic scenarios, reflecting heterogeneous user needs, fluctuating energy demands, and distributed resources. The results demonstrate that the system can effectively model user behavior, support community-level cooperation, and enhance the overall efficiency and intelligence of the intelligent building. The results of this dissertation contributed to six scientific publications, including one journal with an impact factor of 6.6.
The use and integration of intelligent models in buildings can transform the users’ experiences inside the building, being able to provide optimization of spaces and efficient ways to use and interact with the building resources. The use of these intelligent solutions brings some challenges that must be studied, such as the heterogeneity among resources and the need of retrofitting to update the already existing buildings to the concept of intelligent buildings. Although intelligent buildings can revolutionize how people use and interact with spaces, the collective of buildings, creating communities, brings new opportunities to enable interconnected members to pursue common goals, modeling cooperative, collaborative, and, sometimes, competitive roles. This new dynamic where organic systems can communicate and interact also raises challenges regarding the modeling of users, users’ preferences, and the existence of common infrastructures to enable the implementation of intelligent models at community, building, and user levels. This dissertation aims to conceive, implement, test, and validate a container-based infrastructure, entitled Caravels, which combines the concepts of intelligent communities and intelligent buildings to develop a context-aware solution that considers different users and different buildings. The conceived solution, Caravels, employs a distributed architecture for the intelligent management of citizen intelligent communities, where each member operates as an autonomous and intelligent entity, while remaining interconnected through a shared infrastructure. The architecture enables both local and community-level services, meaning that a member can integrate individual services, specifically chosen for that user, while still contributing to and benefiting from community-level optimizations. Central to this work is the modeling of user preferences in complex, dynamic, and multi-user environments. The dissertation explores the psychological and cognitive challenges of preference representation, recognizing that users often struggle to articulate or prioritize their own preferences. The proposed models can adapt over time, incorporating feedback and behavioral data to support proactive and context-aware decision-making. Artificial intelligence techniques, including supervised, unsupervised, and reinforcement learning, are integrated across the system to enable predictive analytics, optimization, and autonomous control. To validate the proposed architecture and methodologies, several case studies were conducted in realistic scenarios, reflecting heterogeneous user needs, fluctuating energy demands, and distributed resources. The results demonstrate that the system can effectively model user behavior, support community-level cooperation, and enhance the overall efficiency and intelligence of the intelligent building. The results of this dissertation contributed to six scientific publications, including one journal with an impact factor of 6.6.
Description
Keywords
container-based architecture distributed computing intelligent communities intelligent buildings intelligent models user modeling user preferences Arquitetura de containers Computação distribuída Comunidades inteligentes Edifícios inteligentes Modelos inteligentes Modelação de preferências do utilizador