Thermal performance prediction of street trees inside isolated open spaces – evaluations from real scale retrofitting project

Zeeshan, Muhammad; Ali, Zaib; Uddin, Emad

doi:10.1080/19401493.2022.2038270

Cited by 11 publications

(5 citation statements)

References 75 publications

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“…According to the results presented in section 3, it can be concluded that the most influential environmental parameters on OTC in this setup were vegetation (Cheung & Jim, 2018;Karimi et al, 2020;Xiao & Yuizono, 2022;Zeeshan et al, 2022), and street orientation (Abdollahzadeh & Biloria, 2021;Ren et al, 2022). By comparing different cases and further studying data it can be concluded that the most important factor in OTC in this context is radiation (Lai et al, 2019(Lai et al, , 2020.…”

Section: Discussion and Future Workmentioning

confidence: 72%

A novel machine learning-based framework for mapping outdoor thermal comfort

Shahrestani

Zomorodian

Karami

et al. 2022

Advances in Building Energy Research

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Rapid urbanization and global warming have increased heat stress in urban areas. This in turn makes using indoor space more compelling and leads to more energy consumption. Therefore, paying attention to outdoor spaces design with thermal comfort in mind becomes more important since outdoor spaces can host a variety of activities. This research aims to introduce a machine learningbased framework to predict the effects of different urban configurations (i.e. different greening configurations and types, different façade materials, and different urban geometry) on outdoor thermal comfort through training a pix2pix Convolutional generative adversarial network (cGAN) model. For the training of the machine learning model, a dataset consisting of 208 coupled pictures of input and output has been created. The simulation of this data has been carried out by ENVI-met. The resulting machine learning model had a Structural Similarity Index (SSIM) of 96% on the test dataset with the highest SSIM of 97.08 and lowest of 94.43 which shows the high accuracy of the model and it could have reached an answer in 3 s compared to the 30-min average time for ENVI-met simulation. The resulting model shows great promise for assisting researchers and urban designers in studying existing urban contexts or planning new developments. HIGHLIGHTS. Machine learning use in outdoor thermal comfort assessment has been investigated. . Vegetation, urban geometry, surface albedo, and water bodies have been studied parameters. . Vegetation and street orientation have the highest and water bodies have the least impact on outdoor thermal comfort. . Pix2pix algorithm implementation could create thermal comfort maps with 96% SSIM.

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Section: Discussion and Future Workmentioning

confidence: 72%

A novel machine learning-based framework for mapping outdoor thermal comfort

Shahrestani

Zomorodian

Karami

et al. 2022

Advances in Building Energy Research

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“…Fifteen different unsteady cases (Tabulated in Annexure A of Supplementary Information) were modelled based on the prevalent wind, humidity and temperature conditions for the heatwave period (06/18/2015–06/22/2015). The results of the RC are validated against the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data (NASA, 2015) as have been widely adapted to validate simulation results in urban microclimates (Antoniou et al, 2019; Toparlar et al, 2018; Zeeshan et al, 2022). The evaluation is made at three different times of the day, as shown in Figure 2(a–c) and then averaged to obtain a single value over the region.…”

Section: Resultsmentioning

confidence: 99%

The Potential of Cool Materials Towards Improving Thermal Comfort Conditions Inside Real-urban Hot-humid Microclimate

Zeeshan

Ali

2022

Environment and Urbanization ASIA

Self Cite

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The urban heat island (UHI) phenomenon has become a major concern for city sustainability in the wake of global warming and rapid urbanization. This has resulted in increased heat stress and worsened outdoor thermal comfort in urban microclimates. The study demonstrates that heat stress can be mitigated by reducing air temperature and surface temperature with the integration of cool materials into the urban environment using computational fluid dynamics, under representative climatic conditions. The thermal comfort conditions are examined at pedestrian height and the diverse vertical levels by evaluating two climatic parameters: first, the reduction of ambient air temperature and surface temperature characterizing the mitigation (cooling) intensity, and second, the alteration of airflow velocity. The analysis shows that the proposed intervention can effectively decrease surrounding temperature and promote airflow. Cool materials, when applied simultaneously on both buildings and ground, generate a more pronounced mitigation effect than on only the ground or the buildings as it results in a large reduction of air and surface temperature, that is, of 2°C and 6°C, respectively. For airflow velocity, it is highest for combined cool materials, with maximum effect at the time of highest solar irradiance. The study is expected to inform architects, urban designers and people involved in construction engineering regarding retrofitting mitigation of UHI under representative climatic conditions.

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“…One study has been conducted using the unsteady Reynolds-averaged Navier-Stokes (URANS) equations for computational fluid dynamics (CFD) simulations to analyze the cooling effect of street trees during heatwaves in urban environments. It was found that vegetation can effectively lower the surrounding temperature, thereby reducing energy consumption and promoting thermal comfort conditions [61]. Furthermore, in terms of human-perceived thermal comfort, pedestrians generally tend to choose walking and sitting positions with high thermal comfort, such as shaded areas on sidewalks.…”

Section: Urban Development Elements and The Urban Heat Environmentmentioning

confidence: 99%

Study of the Correlation between the Urban Wind–Heat Environment and Urban Development Elements in High-Density Urban Areas: A Case Study of Central Shanghai

Li,

Yang,

Zhao

et al. 2024

Buildings

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To prevent damage to human life and health caused by high temperatures and extreme weather and to promote sustainable urban development, it is necessary to optimize the layout of urban development elements to improve the urban wind–heat environment. Taking the high-density urban area of Shanghai as an example, this study used wavelet analysis to investigate the cyclic changes of the average annual temperature of Shanghai from 1950 to 2022 and the average annual wind speed of Shanghai from 2000 to 2020. The correlation between the urban heat environment and the urban development elements was analyzed using geographically weighted regression. The correlation was then examined using BP neural network, and finally, the impacts of different urban spatial patterns on the wind environment were analyzed using CFD numerical simulation. The results show that the average annual temperature of Shanghai city has an overall upward trend, with significant cycles of 44 and 32 years that are shortening over time. The average annual wind speed has a downward trend, with a significant main cycle of 22 years. Greening and water coverage, as well as the floor area ratio, have a significant reducing effect on surface temperature, whereas building density is positively correlated with surface temperature. Building density has a significant reducing effect on wind speed, whereas the effect of floor area ratio is not significant. The effect of building density on wind speed is significantly weakened, whereas the effect of the floor area ratio is not significant. This study provides valuable references for Shanghai and other high-density cities to optimize urban spatial patterns in order to improve the safety and comfort of the urban wind–heat environments. This study is of significant importance and value in promoting sustainable urban development, protecting the health of urban residents, and advancing spatial justice and equal well-being.

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Thermal performance prediction of street trees inside isolated open spaces – evaluations from real scale retrofitting project

Cited by 11 publications

References 75 publications

A novel machine learning-based framework for mapping outdoor thermal comfort

A novel machine learning-based framework for mapping outdoor thermal comfort

The Potential of Cool Materials Towards Improving Thermal Comfort Conditions Inside Real-urban Hot-humid Microclimate

Study of the Correlation between the Urban Wind–Heat Environment and Urban Development Elements in High-Density Urban Areas: A Case Study of Central Shanghai

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