A control and decision system for smart buildings using wireless sensor and actuator networks

Farias, Claudio M. de; Soares, Henrique; Pìrmez, Luci; Delicato, Flávia C.; Santos, Igor Leão dos; Carmo, Luiz Fernando Rust da Costa; Souza, José Neuman de; Zomaya, Albert Y.; Döhler, Mischa

doi:10.1002/ett.2791

Cited by 35 publications

(16 citation statements)

References 23 publications

(61 reference statements)

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“…Actuators can be electric switches, relays, engines, etc. Their role is to turn on/off Lamps, Refrigerators or other appliances in the Smart Building [9]. So, using sensed data from sensor nodes, actuators can perform actions accordingly [10].…”

Section: Hardware Elements For the Smart Buildingmentioning

confidence: 99%

Designing energy efficient Smart Buildings in ubiquitous environments

Abdennadher

Khabou

Rodriguez

et al. 2015

2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)

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Smart Buildings have been the subject of research for more than two decades. The different perspectives range from monitoring and controlling energy consumption. Indeed, energy efficiency is considered as one of the promising fields of applications of the Internet of Things. Since Smart Buildings are an important part of the smart grid, their energy efficiency is vital for the environment and global sustainability. In ubiquitous computing systems, designing Smart Building applications is a challenge since these applications must ensure a trade-ofT between energy consumption and occupants comfort especially in ubiquitous environments. In this paper, we focus on the design of a Smart Building application in ubiquitous computing systems. TheSmart Building application is able to manage energy consumption while keeping the occupants comfort. We elaborate an illustrative scenario to demonstrate the usefulness of our work.

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Section: Hardware Elements For the Smart Buildingmentioning

confidence: 99%

Designing energy efficient Smart Buildings in ubiquitous environments

Abdennadher

Khabou

Rodriguez

et al. 2015

2015 15th International Conference on Intelligent Systems Design and Applications (ISDA)

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“…Smart objects function based on sensing and actuation and they consist of sensors, microprocessors and actuators in order to connect with the surrounded environment in the building so that they can react accordingly [13,14]. Sensors read the real-time status of the environment and pass the input as a signal through a network for processing [15].…”

Section: Categorization Of Smart Componentsmentioning

confidence: 99%

An Experimental Investigation of the Integration of Smart Building Components with Building Information Model (BIM)

Afsari¹,

Flórez²,

Maneke³

et al. 2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Building Information Modeling (BIM) is a methodology to digitally represent all the physical and functional characteristics of a building. Importantly, in smart buildings smart components that are enabled with sensing and actuation need to be modeled accurately within the BIM model. This data representation needs to include multiple status of the smart component based on their performance to guide the design and construction process. However, currently there is not a clear methodology or guideline on how to embed smart components in the BIM model. Visualization techniques have been developed based on CAD technology to integrate smart components in the building model but these techniques have not been applied to BIM environment. To accurately model smart components, the component must be more than a single status representation and must contain complete and accurate dynamic data of the smart component. In this research, data properties, visualization techniques, and categorization of smart components is investigated. Then, through an experimental investigation, nine smart components across five building disciplines are modeled and embedded in a BIM model of a smart space. The model includes parameters that facilitate the data representation of the smart components. Data properties, data organization, and simulation of the smart component within the building model is explained. Challenges and future research is discussed.

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“…To capture complex, nonlinear, and multivariable interactions, mathematical approaches such as Gaussian processes have been utilized [22,23], multi-agent decision-making control strategies [24], and Bayesian-calibrated energy models [25,26]. Furthermore, with the proliferation of wireless sensor networks in smart buildings [27], interest in assessing performance has extended beyond energy into mold growth and remediation [28], as well as disaster preparedness and management [29,30] for events such as fires [31], earthquakes [32], and bioterrorist attacks [33]. It is clear from the literature that the need for decision support within operational building settings is vast, yet a balance between risk and situational usefulness needs to be attained.…”

Section: Literature Reviewmentioning

confidence: 99%

An energy signal tool for decision support in building energy systems

Henze¹,

Pavlak²,

Florita³

et al. 2015

Applied Energy

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We demonstrate an energy signal tool for commercial buildings. The tool estimates energy use for different end uses. It accounts for uncertainty in model parameters and inputs. An indicator (stoplight color) is chosen to minimize misclassification cost. Parameter distributions are adjusted over time via Bayesian updating. a b s t r a c t A prototype energy signal tool is demonstrated for operational whole-building and system-level energy use evaluation. The purpose of the tool is to give a summary of building energy use which allows a building operator to quickly distinguish normal and abnormal energy use. Toward that end, energy use status is displayed as a traffic light, which is a visual metaphor for energy use which is substantially different from expected (red and yellow lights) or more or less the same as expected (green light). Which light to display for a given energy end-use is determined by comparing expected energy use to actual energy use. As expected energy use is necessarily uncertain, we cannot choose the appropriate light with certainty. Instead the energy signal tool chooses the light by minimizing the expected cost of displaying the wrong light. The expected energy use is represented by a probability distribution. Energy use is modeled by a low-order lumped parameter model. Uncertainty in energy use is quantified by a Monte Carlo exploration of the influence of model parameters on energy use. Distributions over model parameters are updated over time via Bayes' theorem. The simulation study is devised to assess whole building energy signal accuracy in the presence of uncertainty and faults at the submetered level, which may lead to tradeoffs at the whole building level not detectable without submetering.Published by Elsevier Ltd.

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A control and decision system for smart buildings using wireless sensor and actuator networks

Cited by 35 publications

References 23 publications

Designing energy efficient Smart Buildings in ubiquitous environments

Designing energy efficient Smart Buildings in ubiquitous environments

An Experimental Investigation of the Integration of Smart Building Components with Building Information Model (BIM)

An energy signal tool for decision support in building energy systems

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