Enhanced building thermal model by using CO2 based occupancy data

Imanishi, Tomoya; Tennekoon, Rajitha; Pálenský, Peter; Nishi, Hiroaki

doi:10.1109/iecon.2015.7392578

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…The challenge arises, however, while defining the lumped model's parameter values [120]. When operational data (measured sensor data) is unavailable and only thermo-physical characteristics are available, the parameters can be determined analytically [121,122], or by developing an analytical model as a reference model using available data and matching the resultant dynamics of the thermal network and reference models [74,80]. However, if there is good availability of measured data but a lack of thermophysical characteristics data, inverse methodologies [123][124][125] are applied to determine parameter values by minimizing the prediction error between the thermal-network model and the measured data [126][127][128][129].…”

Section: Parametric Identificationmentioning

confidence: 99%

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

2022

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The development of smart buildings, as well as the great need for energy demand reduction, has renewed interest in building energy demand prediction. Intelligent controllers are a solution for optimizing building energy consumption while maintaining indoor comfort. The controller efficiency on the other hand, is mainly determined by the prediction of thermal behavior from building models. Due to the development complexity of the models, these intelligent controllers are not yet implemented on an industrial scale. There are primarily three types of building models studied in the literature: white-box, black-box, and gray-box. The gray-box models are found to be robust, efficient, of low cost computationally, and of moderate modeling complexity. Furthermore, there is no standard model configuration, development method, or operation conditions. These parameters have a significant influence on the model performance accuracy. This motivates the need for this review paper, in which we examined various gray-box models, their configurations, parametric identification techniques, and influential parameters.

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Section: Parametric Identificationmentioning

confidence: 99%

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

2022

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“…Among the candidate sensors to be paired with occupancy detection sensors are environmental sensors, which obtain occupancy information through changes in environmental readings in a local proximity. Environmental assessment WSNs, such as those for indoor air quality applications [10, 11] and smart climate control systems [7, 8, 12, 13], make use of sensors that can observe volatile organic compounds, temperature, and humidity. By not focusing on specific users, the environmental methods satisfy anonymity and the indirect nature avoids requiring user attention and interaction.…”

Section: Related Workmentioning

confidence: 99%

“…A common metric exploited is the change in CO 2 production relative to the number of persons present. This relation is dependent on the deployment space, and as such, requires either knowledge of the target space to model the relation explicitly [12, 14, 15], or the ability to learn the relation from observation [9, 14, 16]. The required prior knowledge of the former model restricts the generalisation of a solution and would hinder any redeployment for a WSN; thus, a learned solution is preferable.…”

Section: Related Workmentioning

confidence: 99%

Designing learned CO ₂ ‐based occupancy estimation in smart buildings

Brennan

Taylor

Spachos

2018

IET wirel. sens. syst.

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Many applications, such as smart buildings, crowd flow, action recognition, and assisted living, rely on occupancy information. Although the use of smart cameras and computer vision can assist with these tasks and provide accurate occupancy information, it can be cost prohibitive, invasive, and difficult to scale or generalise to different environments. An alternative solution should bring similar accuracy while minimising the listed problems. This work demonstrates that a scalable wireless sensor network with CO 2-based estimation is a viable alternative. To support many applications, a solution must be transferable and must handle not knowing the physical system model; instead, it must learn to model CO 2 dynamics. This work presents a viable prototype and uses the captured data to train machine learning-based occupancy estimation systems. Models are trained under varying conditions to assess the consequences of design decisions on performance. Four different learning models were compared: gradient boosting, k-nearest neighbours (KNN), linear discriminant analysis, and random forests. With sufficient labelled data, the KNN model produced peak results with a root-mean-square error value of 1.021.

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“…Hence, occupancy can be detected by analyzing the sensor data. Another example of such systems is the Management of Energy Consumption and Heat Ventilation and Air Conditioning (HVAC) [1], [2] in Smart Buildings. To be specific, information from occupancy detection and occupant's behavioral patterns can be used to manage and control buildings more intelligently for ventilation, heating and cooling, and energy efficiency [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Occupancy Detection in Room Using Sensor Data

Toutiaee

2021

Preprint

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Purpose-With the advent of Internet of Thing (IoT), and ubiquitous data collected every moment by either portable (smart phone) or fixed (sensor) devices, it is important to gain insights and meaningful information from the sensor data in context-aware computing environments. Many researches have been implemented by scientists in different fields, to analyze such data for the purpose of security, energy efficiency, building reliability and smart environments. One study, that many researchers are interested in, is to utilize Machine Learning techniques for occupancy detection where the aforementioned sensors gather information about the environment. This paper provides a solution to detect occupancy using sensor data by using and testing several variables. Additionally we show the analysis performed over the gathered data using Machine Learning and pattern recognition mechanisms is possible to determine the occupancy of indoor environments. Seven famous algorithms in Machine Learning, namely as Decision Tree, Random Forest, Gradient Boosting Machine, Logistic Regression, Naive Bayes, Kernelized SVM and K-Nearest Neighbors are tested and compared in this study.

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Enhanced building thermal model by using CO2 based occupancy data

Cited by 6 publications

References 13 publications

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

Designing learned CO ₂ ‐based occupancy estimation in smart buildings

Occupancy Detection in Room Using Sensor Data

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Enhanced building thermal model by using CO2 based occupancy data

Cited by 6 publications

References 13 publications

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

Designing learned CO 2 ‐based occupancy estimation in smart buildings

Occupancy Detection in Room Using Sensor Data

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Product

Resources

About

Designing learned CO ₂ ‐based occupancy estimation in smart buildings