BIM- and IoT-based virtual reality tool for real-time thermal comfort assessment in building enclosures

Shahinmoghadam, Mehrzad; Natephra, Worawan; Motamedi, Ali

doi:10.1016/j.buildenv.2021.107905

Cited by 84 publications

(37 citation statements)

References 32 publications

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“…Proc. 2021, 11, 25 2 of 5 a fundamental role in generating an accurate representation of the building environment by creating digital versions of the actual buildings [7]. In this work, the calibrated building energy models are used as a source of virtual variables data that are used to enhance the predictive capability of machine learning and, at the same time, as a source of data regarding different operational scenarios of the building.…”

Section: Methodsmentioning

confidence: 99%

“…Some environmental variables useful for the training of machine learning methods are difficult and expensive to measure by sensors (e.g., operative temperature, mean radiant temperature, and surface temperature) [6]. Digital twins intended as calibrated building energy models cover a fundamental role in generating an accurate representation of the building environment by creating digital versions of the actual buildings [7]. In this work, the calibrated building energy models are used as a source of virtual variables data that are used to enhance the predictive capability of machine learning and, at the same time, as a source of data regarding different operational scenarios of the building.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Innovative Modelling Approach Based on Building Physics and Machine Learning for the Prediction of Indoor Thermal Comfort in an Office Building

Tardioli¹,

Filho²,

Bernaud

et al. 2021

The 9th Annual Edition of Sustainable Places (SP 2021)

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In this paper, an innovative hybrid modelling technique based on machine learning and building dynamic simulation is presented for the prediction of indoor thermal comfort feedback from occupants in an office building in Le Bourget-du-Lac, Chambéry, France. The office was equipped with Internet of Things (IoT) environmental sensors. A calibrated building energy model was created for the building using optimisation tools. Thermal comfort was collected using a portable device. A machine learning (ML) model was trained using collected feedback, environmental data from IoT devices and synthetic datasets (virtual sensors) extracted from a physics-based model. A calibrated energy model was used in co-simulation with the predictive method to estimate comfort levels for the building. The results show the ability of the method to improve the prediction of occupant feedback when compared to traditional thermal comfort approaches of about 25%, the importance of information extracted from the physics-based model and the possibility of leveraging scenario evaluation capabilities of the dynamic simulation model for control purposes.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Innovative Modelling Approach Based on Building Physics and Machine Learning for the Prediction of Indoor Thermal Comfort in an Office Building

Tardioli¹,

Filho²,

Bernaud

et al. 2021

The 9th Annual Edition of Sustainable Places (SP 2021)

View full text Add to dashboard Cite

show abstract

“…The authors used Arduino UNO to collect measurements from LCSs of temperature, humidity, and light level. Shahinmoghadam et al studied the synergistic benefits of BIM, IoT, and virtual reality (VR) for establishing a low-cost monitoring thermal comfort system [209]. There are diverse research lines in the literature dealing with application of BIM to low-cost monitoring of buildings.…”

Section: Integration Of Bim and Low-cost Sensorsmentioning

confidence: 99%

Application of Low-Cost Sensors for Building Monitoring: A Systematic Literature Review

et al. 2021

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In recent years, many scholars have dedicated their research to the development of low-cost sensors for monitoring of various parameters. Despite their high number of applications, the state of the art related to low-cost sensors in building monitoring has not been addressed. To fill this gap, this article presents a systematic review, following well-established methodology, to analyze the state of the art in two aspects of structural and indoor parameters of buildings, in the SCOPUS database. This analysis allows to illustrate the potential uses of low-cost sensors in the building sector and addresses the scholars the preferred communication protocols and the most common microcontrollers for installation of low-cost monitoring systems. In addition, special attention is paid to describe different areas of the two mentioned fields of building monitoring and the most crucial parameters to be monitored in buildings. Finally, the deficiencies in line with limited number of studies carried out in various fields of building monitoring are overviewed and a series of parameters that ought to be studied in the future are proposed.

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“…Particular attention should be paid to the use of BIM in DTs, a phenomenon that has been growing and organically developing in the last period taking on the lesson learned in the manufacturing sector [37]. A remarkable example is provided by Shahinmoghadam et al [38]; the authors develop a BIM-IoT-based DT that also makes use of virtual reality for thermal and comfort monitoring. In [39], Desogus et al analysed the use of BIM and IoT in a DT framework for monitoring of indoor conditions (not only temperature and comfort but also luminance, etc.)…”

Section: Buildingsmentioning

confidence: 99%

A comprehensive review on digital twins for smart energy management system

Lamagna¹,

Groppi²,

Nezhad³

et al. 2021

Int. J. EQ

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Energy systems digitalisation represents the energy sector's future, and Digital Twins represent the most advanced and complete way to monitor and optimally manage a complex system such as the upcoming solutions. Those latter will comprehend several energy generators, traditional and/or from renewable energy sources (RESs), different energy storage systems using several energy vectors and that interconnect different energy-consuming sectors (power, thermal, transport sectors) and that fully exploit the potential synergies offered by such interconnected system. Nevertheless, since the first conceptualisation of digital twins in the first years of the 21st century, its use has not started yet for different reasons that are affecting the adoption of this game-changer approach. Hence, what are the main barriers that are holding back the adoption of digital twins in smart energy systems? The present review paper answers this research question while discussing the case studies that can be found in literature and analysing the different approaches and the system architectures that have been tested or simply idealised. This paper provides a basis for future research that aims at applying the digital twin concept in the energy sector and particularly for power grid management. It deals with the challenges of big data management, the ones related to real-time measurements and continuous communication between the real-world system and its digital twin, the investment for measuring systems, the issues connected with the use of large data centres and the correlated energy-related challenges and doubts. The review analyses the challenges that have been encountered so far, the proposed solutions and the opportunities that such a 'work in progress' topic offers.

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BIM- and IoT-based virtual reality tool for real-time thermal comfort assessment in building enclosures

Cited by 84 publications

References 32 publications

An Innovative Modelling Approach Based on Building Physics and Machine Learning for the Prediction of Indoor Thermal Comfort in an Office Building

An Innovative Modelling Approach Based on Building Physics and Machine Learning for the Prediction of Indoor Thermal Comfort in an Office Building

Application of Low-Cost Sensors for Building Monitoring: A Systematic Literature Review

A comprehensive review on digital twins for smart energy management system

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