A machine-learning-based method for thermal design optimization of residential buildings in highly urbanized areas of Turkey

Yigit, Sadik

doi:10.1016/j.jobe.2021.102225

Cited by 27 publications

(8 citation statements)

References 44 publications

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“…They employed function-driven deep learning to develop conceptual design, and then trained deep neural networks to evaluate existing graph-encoded designs, extract subgraphs, and integrate them. Yigit [155] and Huang et al [156] used the machine learning method to optimize the residential buildings for minimizing building energy consumption. This machine learning could train for passive design optimization of the green roof conducted by Lin et al [157].…”

Section: Machine Learning For Dfammentioning

confidence: 99%

Design for Manufacture and Assembly (DfMA) of Digital Fabrication (Dfab) and Additive Manufacturing (AM) in Construction: A Review

Tuvayanond¹,

Prasittisopin²

2023

Preprint

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Design for Manufacture and Assembly (DfMA) in architectural, engineering, and construction (AEC) industry is attracting the attention of designers, practitioners, and construction project stakeholders. Digital fabrication (Dfab) and design for additive manufacturing (DfAM) practices are found in current needs for further research and development. The DfMA's conceptual function is to maximize the process efficiency of Dfab and AM building projects. This work reviewed 171 relevant research articles over the past few decades. The concept of DfMA and the fundamentals of DfMA in building and construction were explored. In addition, DfMA procedures associated with Dfab and DfAM, as well as its AM assembly process, were discussed. Lastly, the current machine learning research on DfMA in construction was also highlighted. Large research gaps in the DfMA for Dfab and DfAM can be filled to increase operational efficiency and sustainable practices.

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Section: Machine Learning For Dfammentioning

confidence: 99%

Design for Manufacture and Assembly (DfMA) of Digital Fabrication (Dfab) and Additive Manufacturing (AM) in Construction: A Review

Tuvayanond¹,

Prasittisopin²

2023

Preprint

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“…It has windows with a 33% Window-to-Wall Ratio (WWR) on all fronts. Since various WWRs have been used in different studies [58][59][60], in this article, approximately the average of these intervals is assumed. For the east and west fronts, three windows besides the north and south, four windows with 0.80m sill height, and 2m height are considered as the room has a rectangular plan and elongation in the east-west axis.…”

Section: Set Up For Reading Room Modelmentioning

confidence: 99%

Impact of Different Shading Devices on Daylight Performance and Visual Comfort of A Four Opening Sides' Reading Room In Rasht

Dabaj¹,

Rahbar²,

Fakhr³

2022

Journal of Daylighting

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Daylighting has become an essential feature in libraries since it can boost productivity, well-being, and energy savings. It is crucial to prevent discomfort glare irritation while maintaining the quality of view, and daylight demands. This study compares four different shading systems on all fronts of a reading room. Shading devices include a Perforated Solar Screen (PSS), besides three Venetian Blinds typologies (exterior, interior, and exterior integrated with overhangs) are generated considering various parameters. The 60-min time step for an entire year is used to find out the worst dates for glare analysis in different positions for Rasht, Iran. The findings of Daylight Autonomy (DA), Useful Daylight Illuminance (UDI), Daylight Glare Probability (DGP), and Quality of View (QV) depict that PSS is ineffective at glare reduction but is adequate for annual metrics and view quality. Moreover, Venetian Blinds in every three typologies are sufficient to deal with glare problems on most dates, despite differences in their characteristics being reported. Annual metrics and Quality of View in these cases, depending on variables, are changeable.

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“…A study about urbanized areas [14] aimed at developing a surrogate model-based integrated optimization system to obtain energy-optimal thermal designs for residential buildings in the most urbanized cities in Turkey under different levels of budget constraints.…”

Section: Related Workmentioning

confidence: 99%

Applicability of Multivariate Linear Regression in Building Energy Demand Estimation

Storcz¹,

Kistelegdi²,

Horváth³

et al. 2022

MMEP

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The vision of the research project is to find an energy optimal building configuration, suitable for specified requirements and restrictions. The first step on this way is to create a measure to compare building configurations, faster than explicit energetic simulations. The current study examines the applicability of multivariate linear regression to support the solution of building optimization problems. During the study, multivariate linear regression models were created to estimate the expected annual heating energy demand of building configurations and examined their accuracy Between examinations, the models were modified so that the complexity was increased only to such an extent that the approximation was still sufficiently accurate. The result was a multivariate linear model that estimated the expected output for unknown descriptive variables with a 0% relative error and a 1.6% standard deviation. The R2 point of the estimates was 0.9884. Based on these, the model was considered applicable in the search space defined by the training patterns.

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A machine-learning-based method for thermal design optimization of residential buildings in highly urbanized areas of Turkey

Cited by 27 publications

References 44 publications

Design for Manufacture and Assembly (DfMA) of Digital Fabrication (Dfab) and Additive Manufacturing (AM) in Construction: A Review

Design for Manufacture and Assembly (DfMA) of Digital Fabrication (Dfab) and Additive Manufacturing (AM) in Construction: A Review

Impact of Different Shading Devices on Daylight Performance and Visual Comfort of A Four Opening Sides' Reading Room In Rasht

Applicability of Multivariate Linear Regression in Building Energy Demand Estimation

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