TY - GEN
T1 - Spatio-temporal Data Analysis for Development of Microclimate Prediction Models
AU - Hong, Tageui
AU - Heo, Yeonsook
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1A2C1003751).
Publisher Copyright:
© International Building Performance Simulation Association, 2022
PY - 2022
Y1 - 2022
N2 - Microclimate prediction models have been developed to consider the effects of urban variables on urban microclimate. However, existing studies have not fully exploited the spatio-temporal microclimate data and focused on either spatial or temporal aspects of microclimate conditions. In this study, we analyze the characteristics of full spatio-temporal data of 246 weather stations in Seoul, Korea through the widely used multiple linear regression and Gaussian process regression. We created a set of datasets with different levels of spatial-temporal variability and evaluated the suitability of the two methods and the characteristics of the microclimate data. The statistical analysis results indicate that the accuracy of predicting the urban heat island (UHI) intensity depends on a level of variability contained in the spatio-temporal data and the two methods cannot fully explain the effect of meteorological and urban variables on the UHI phenomena. The results suggest the need to develop an appropriate modelling methodology that can accurately capture full variability in the spatial-temporal data of microclimate conditions.
AB - Microclimate prediction models have been developed to consider the effects of urban variables on urban microclimate. However, existing studies have not fully exploited the spatio-temporal microclimate data and focused on either spatial or temporal aspects of microclimate conditions. In this study, we analyze the characteristics of full spatio-temporal data of 246 weather stations in Seoul, Korea through the widely used multiple linear regression and Gaussian process regression. We created a set of datasets with different levels of spatial-temporal variability and evaluated the suitability of the two methods and the characteristics of the microclimate data. The statistical analysis results indicate that the accuracy of predicting the urban heat island (UHI) intensity depends on a level of variability contained in the spatio-temporal data and the two methods cannot fully explain the effect of meteorological and urban variables on the UHI phenomena. The results suggest the need to develop an appropriate modelling methodology that can accurately capture full variability in the spatial-temporal data of microclimate conditions.
UR - http://www.scopus.com/inward/record.url?scp=85151566736&partnerID=8YFLogxK
U2 - 10.26868/25222708.2021.30781
DO - 10.26868/25222708.2021.30781
M3 - Conference contribution
AN - SCOPUS:85151566736
T3 - Building Simulation Conference Proceedings
SP - 878
EP - 885
BT - BS 2021 - Proceedings of Building Simulation 2021
A2 - Saelens, Dirk
A2 - Laverge, Jelle
A2 - Boydens, Wim
A2 - Helsen, Lieve
PB - International Building Performance Simulation Association
T2 - 17th IBPSA Conference on Building Simulation, BS 2021
Y2 - 1 September 2021 through 3 September 2021
ER -