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. 2022 Aug 9:10:992050.
doi: 10.3389/fpubh.2022.992050. eCollection 2022.

Contribution of local climate zones to the thermal environment and energy demand

Ruxin Yang  1 , Jun Yang  1   2   3 , Lingen Wang  4 , Xiangming Xiao  5 , Jianhong Xia  6
Affiliations

Contribution of local climate zones to the thermal environment and energy demand

Ruxin Yang et al. Front Public Health. .

Abstract

Urban heat islands (UHIs) and their energy consumption are topics of widespread concern. This study used remote sensing images and building and meteorological data as parameters, with reference to Oke's local climate zone (LCZ), to divide urban areas according to the height and density of buildings and land cover types. While analyzing the heat island intensity, the neural network training method was used to obtain temperature data with good temporal as well as spatial resolution. Combining degree-days with the division of LCZs, a more accurate distribution of energy demand can be obtained by different regions. Here, the spatial distribution of buildings in Shenyang, China, and the law of land surface temperature (LST) and energy consumption of different LCZ types, which are related to building height and density, were obtained. The LST and energy consumption were found to be correlated. The highest heat island intensity, i.e., UHILCZ 4, was 8.17°C. The correlation coefficients of LST with building height and density were -0.16 and 0.24, respectively. The correlation between urban cooling energy demand and building height was -0.17, and the correlation between urban cooling energy demand and building density was 0.17. The results indicate that low- and medium-rise buildings consume more cooling energy.

Keywords: air temperature inversion; degree-days; energy consumption; neural network; urban heat island.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The location of the study area.
Figure 2
Figure 2
LCZ distribution in study area. LCZ, Local climate zone.
Figure 3
Figure 3
Land surface temperature distribution.
Figure 4
Figure 4
Distribution of LST in LCZ (LST unit:°C). LST, land surface temperature. LCZ, local climate zone. The results are sampled and counted according to the calculation results associated with the LCZ grids.
Figure 5
Figure 5
Distribution of CDD in LCZ (CDD unit:°C d). CDD, cooling degree-days. The results are sampled and counted according to the calculation results associated with the grid of the different LCZ plots.
Figure 6
Figure 6
Correlation between LST (CDD) and building density (LST unit:°C).
Figure 7
Figure 7
Correlation between LST (CDD) and building height (CDD unit:°C d).
Figure 8
Figure 8
CDD of study area. CDD, cooling degree-days.
See this image and copyright information in PMC

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