Semantic models for physical processes in CPS at the example of occupant thermal comfort

Ploennigs, Joern; Clement, Jana; Wollschlaeger, Bastian; Kabitzsch, Klaus

doi:10.1109/isie.2016.7745039

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…Frešer et al [30] created a decision support system by anticipating IEQ parameters using a semantic digital twin. Donkers et al [22], Esnaola-Gonzalez et al [25], Corry et al [17], and Ploennigs et al [69] used semantic digital twins to monitor thermal comfort in buildings.…”

Section: Donkers Et Al / Creating Occupant-centered Digital Twins Usi...mentioning

confidence: 99%

“…Semantic web technologies promise to accomplish higher interoperability levels in the architecture, engineering, and construction (AEC) domain [67]. Multiple researchers applied semantic web technologies to create rich digital representations of buildings [12], some of whom for research related to occupants' comfort [17,22,25,30,64,69]. To better understand the existing promises and challenges of semantic web technologies to solve the interoperability issues, the following subsections give an overview of state-of-the-art semantic web developments related to occupants, their feedback, and their building performance assessments.…”

Section: Occupant Feedback In the Semantic Webmentioning

confidence: 99%

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Creating occupant-centered digital twins using the Occupant Feedback Ontology implemented in a smartwatch app

Donkers

Vries

Yang

2024

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Occupant feedback enables building managers to improve occupants’ health, comfort, and satisfaction. However, acquiring continuous occupant feedback and integrating this feedback with other building information is challenging. This paper presents a scalable method to acquire continuous occupant feedback and directly integrate this with other building information. Semantic web technologies were applied to solve data interoperability issues. The Occupant Feedback Ontology was developed to describe feedback semantically. Next to this, a smartwatch app – Mintal – was developed to acquire continuous feedback on indoor environmental quality. The app gathers location, medical information, and answers on short micro surveys. Mintal applied the Occupant Feedback Ontology to directly integrate the feedback with linked building data. A case study was performed to evaluate this method. A semantic digital twin was created by integrating linked building data, sensor data, and occupant feedback. Results from SPARQL queries gave more insight into an occupant’s perceived comfort levels in the Open Flat. The case study shows how integrating feedback with building information allows for more occupant-centric decision support tools. The approach presented in this paper can be used in a wide range of use cases, both within and without the architecture, building, and construction domain.

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Section: Donkers Et Al / Creating Occupant-centered Digital Twins Usi...mentioning

confidence: 99%

Section: Occupant Feedback In the Semantic Webmentioning

confidence: 99%

Creating occupant-centered digital twins using the Occupant Feedback Ontology implemented in a smartwatch app

Donkers

Vries

Yang

2024

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“…In [34], a semantic model is introduced for representing thermal comfort within a semantic framework designed for automated modeling of cyber-physical systems. In [35], the authors address the modeling of indoor comfort metrics by reusing reference models, allowing for the customization of comfort in cruise cabins.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Machine Learning and Optimization Algorithm for Enhanced User Comfort and Energy Efficiency in Smart Homes

Khan,

Fayaz,

Zaman

et al. 2024

Preprint

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Researchers are always looking to find ways to use energy more efficiently while keeping people comfortable at home. They've tried different methods like fuzzy logic and genetic algorithms to do this. In this paper, we introduce a novel model that combines different techniques like data smoothing, optimization, machine learning, and control algorithms to reach this goal in smart homes. The proposed model is made up of three main parts: smoothing, optimization, and control. The smoothing part uses the Kalman filter to get rid of any unnecessary noise and make sure the data used to predict energy use is clean and smooth. The optimization part uses both genetic with firefly algorithms and artificial neural networks to minimize the gap between the actual environmental conditions and the conditions users find comfortable. It adjusts the comfort settings in real-time using machine learning. The control part uses Mamdani fuzzy logic to make sure energy is delivered in the best way possible to the systems that control temperature, lighting, etc., making the home comfortable and saving energy at the same time. Comparative analyses demonstrate the superiority of the proposed model over existing algorithms, including the Particle Swarm Optimization (PSO) algorithm. Additionally, the paper discusses the importance of adaptive controllers in addressing issues associated with incorrect PID controller selection and highlights the benefits of using the FA-GA in energy optimization and comfort improvement. Overall, the proposed model offers an effective solution for efficient energy consumption optimization and enhanced user comfort in diverse settings through the integration of advanced algorithms and optimization modules.

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“…Similarly, ThinkHome [21] is a semantic model developed to represent the whole SH ecosystem, thus including also some concepts related to comfort. Ploennings et al [22] provided a semantic model for the representation of thermal comfort, within a semantic framework aimed at automatized approaches for modeling cyber-physical systems. In [23] the authors tackled the modeling of some indoor comfort metrics by reusing some reference models to enable customization of comfort in cruise cabins; the ontology is here adopted as a backbone for an IoT framework aimed at monitoring and enhancing the cruise experience.…”

Section: Related Workmentioning

confidence: 99%

ComfOnt: A Semantic Framework for Indoor Comfort and Energy Saving In Smart Homes

et al. 2019

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This work introduces ComfOnt, a semantic framework developed within the context of ambient assisted living, context awareness, and ambient intelligence Italian research projects. ComfOnt leverages knowledge regarding Smart Home inhabitants and their particular needs, the devices deployed inside the domestic environment (appliances, sensors, and actuators), the amount of their energy consumption, and indoor comfort metrics to provide dwellers with customized services. Developed reusing widely adopted ontologies, ComfOnt aims at providing inhabitants with the possibility of having personalized indoor comfort in their living environments and at helping them in scheduling their daily activities requiring appliances; in fact, the proposed semantic framework enables the representation of appliances' energy consumption and the energy profile of the Smart Home, thus assisting the dwellers in avoiding power cuts and fostering energy savings. ComfOnt serves as a knowledge base for a prototypical application (DECAM) dedicated to Smart Home inhabitants; the architecture and the functionalities of DECAM are here presented.Although research in the fields of SH, ambient assisted living (AAL), ambient intelligence (AmI), context awareness (CA), and IoT leveraging semantic formalization of knowledge has acquired a growing importance in the last years, only a few works dedicated to the SH tackled the issue of modeling indoor comfort metrics. This work aims at contributing to the field of domain ontologies dedicated to the representation of knowledge necessary to customize services within an SH, including comfort metrics leveraging on already existing models. The proposed set of domain ontologies arises from the experiences of three Italian research projects funded by the Lombardy Region: i-Zeb, Future Homes for Future Communities (FHfFC), and CasAware. i-Zeb [4] is a project aimed at researching and investigating the use of innovative building materials and technological solutions to foster the implementation of Nearly Zero-Energy Buildings. FHfFC [5] focuses on deploying a semantic architecture to enable a set of assistive services aimed at improving inhabitants' daily lives; the services are addressed to an ageing population and encompass a wide range of fields, such as indoor comfort, device interoperability, and domestic rehabilitation. CasAware [6,7] leverages ontological representation of domestic appliances and their energy consumption to foster energy saving behavior, help the dwellers to better schedule their operations with appliances, and avoid domestic power cuts.The set of domain ontologies developed within these three projects-named ComfOnt-allows the formalization of some fundamental indoor comfort metrics, including Italian regulations and laws, and knowledge regarding sensors, actuators, and measurements; ComfOnt can be adopted to describe environmental conditions within the SH and to trigger comfort actuation after detecting an unsafe or uncomfortable situation, exploiting the results deriving from reasoning proc...

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Semantic models for physical processes in CPS at the example of occupant thermal comfort

Cited by 8 publications

References 17 publications

Creating occupant-centered digital twins using the Occupant Feedback Ontology implemented in a smartwatch app

Creating occupant-centered digital twins using the Occupant Feedback Ontology implemented in a smartwatch app

A Hybrid Machine Learning and Optimization Algorithm for Enhanced User Comfort and Energy Efficiency in Smart Homes

ComfOnt: A Semantic Framework for Indoor Comfort and Energy Saving In Smart Homes

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