Thermal-comfort optimization design method for semi-outdoor stadium using machine learning

Zhang, Ruinan; Liu, Deming; Shi, Ligang

doi:10.1016/j.buildenv.2022.108890

Cited by 17 publications

(10 citation statements)

References 38 publications

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“…The study "Thermal-comfort optimization design method for semi-outdoor stadium using machine learning" aims to reveal the relationship between stadium shape and thermal performance using an artificial neural network approach and genetic algorithms. The simulation results show that the simulation is close to the actual measurement and can be used for stadium optimization, which can be increased by 8.96% [17].…”

Section: Introductionmentioning

confidence: 57%

“…Machine learning algorithm data analysis methods continue to be developed and used in various types of buildings. Numerical computing is one of the strengths of machine learning algorithms [17]. The algorithm can also be used in urban heat island research.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning methods have various forms, often using mathematical calculations and computational fluid dynamics (CFD). The use of various machine learning methods means that research results vary [17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Thermal Comfort Prediction Accuracy with Machine Learning between Regression Analysis and Naïve Bayes Classifier

et al. 2022

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Various data analysis methods can make thermal comfort prediction models. One method that is often used is multiple linear regression statistical analysis. Regression analysis needs to be checked for accuracy with other analytical methods. This study compares the making of a thermal comfort prediction model with regression analysis and naïve Bayes analysis. The research method used quantitative methods for data collection regarding thermal comfort. The thermal comfort variable, consisting of eight independent variables and one dependent variable, was measured at Wonosobo High School, Indonesia. The analysis to make the prediction model was carried out with two different analyses: multiple linear regression analysis and naïve Bayes analysis. The results show that naïve Bayes is more accurate than multiple linear regression analysis.

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Section: Introductionmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

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Thermal Comfort Prediction Accuracy with Machine Learning between Regression Analysis and Naïve Bayes Classifier

et al. 2022

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“…Scientific research has placed significant emphasis on the application of ML techniques in the domains of energy conversion and management, building management, structures, and urban areas, particularly when related to physics. In recent years, the utilization of ML methods for assessing urban air quality, microclimate prediction for wind farms, wind load estimation, and wind pressure prediction has been growing [33,[64][65][66][67][68][69], and research focused on testing various approaches and algorithms has gained momentum in recent years (Table 1).…”

Section: Machine Learning For Wind Estimation In Built Environmentmentioning

confidence: 99%

Machine Learning for Pedestrian-Level Wind Comfort Analysis

Gür,

Karadag

2024

Buildings

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(1) Background: Artificial intelligence (AI) and machine learning (ML) techniques are being more widely employed in the field of wind engineering. Nevertheless, there is a scarcity of research on the comfort of pedestrians in terms of wind conditions with respect to building design, particularly in historic sites. (2) Objectives: This research aims to evaluate ML- and computational fluid dynamics (CFD)-based pedestrian wind comfort (PWC) analysis outputs using a novel method that relies on the sophisticated handling of image data. The goal is to propose a novel assessment method to enhance the efficiency of AI models over different urban scenarios. (3) Methodology: The stages include the analysis of climate data, CFD analysis with OpenFOAM, ML analysis using Autodesk Forma, and comparisons of the CFD and ML results using a novel image similarity assessment method based on the SSIM, MSE, and PSNR metrics. (4) Conclusions: This study effectively demonstrates the considerable potential of utilizing ML as a supplementary tool for evaluating PWC. It maintains a high degree of accuracy and precision, allowing for rapid and effective assessments. The methodology for precise comparison of two visual outputs in the absence of numerical data allows for more objective and pertinent comparisons, as it eliminates any potential distortions. (5) Recommendations: Additional research can explore the integration of ML models with climate data and different case studies, thus expanding the scope of wind comfort studies.

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“…Intending to investigate the impact of the stadium's morphology on thermal comfort (UTCI) during its use cycle, the parametric framework built in Grasshopper used the EnergyPlus and OpenFOAM simulation engines to predict thermal comfort. The evaluated average comfortable seats during the use cycle are fed into an ANN model and the Galapagos genetic algorithm is used for the optimization of three parameters (the stadium's canopy elevation, the facade porosity and the sun-shield slant angle) [70]. Tabadkani et al, in a study on courtyard design, used input and output data obtained through Ladybug and Honeybee simulations to train a deep learning model usable for indoor thermal comfort prediction in courtyard buildings [71].…”

Section: State Of the Art In Microclimate Evaluationmentioning

confidence: 99%

Automating Microclimate Evaluation and Optimization during Urban Design: A Rhino–Grasshopper Workflow

Bedra,

Zheng,

et al. 2023

Sustainability

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Though building-scale energy demand and indoor thermal comfort have been extensively covered by recent studies, the automation of middle- and larger-scale outdoor microclimate evaluation in parametric design is less covered. The relatively slow computation and the need for sophisticated expertise are some of the current issues. This paper proposes a Rhino–Grasshopper custom script to automatically compute spatial indicators for a quick thermal comfort estimation. The Galapagos evolutionary algorithm is used to optimize thermal comfort and select the best combinations of spatial indicators. In a summer case study located in Shantou, China, the proposed workflow was three times faster than a non-automated indicator calculation in ArcGIS, while the optimization method achieved 25% to 33% reduction in land areas under extreme heat stress. This automated process applies to existing states and new urban designs. It is adaptable to customized prediction models under different climatic zones.

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Thermal-comfort optimization design method for semi-outdoor stadium using machine learning

Cited by 17 publications

References 38 publications

Thermal Comfort Prediction Accuracy with Machine Learning between Regression Analysis and Naïve Bayes Classifier

Thermal Comfort Prediction Accuracy with Machine Learning between Regression Analysis and Naïve Bayes Classifier

Machine Learning for Pedestrian-Level Wind Comfort Analysis

Automating Microclimate Evaluation and Optimization during Urban Design: A Rhino–Grasshopper Workflow

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