Analysis of the Characteristics of Heat Island Intensity Based on Local Climate Zones in the Transitional Season of Shenyang

被引：0
作者：
Xi, Tianyu [1 ]
Li, Jin [1 ]
Yang, Nuannuan [1 ]
Liu, Xinyu [1 ]
Guo, Fei [2 ]
机构：
[1] Northeastern Univ, Coll Jangho Architecture, Shenyang 110169, Liaoning, Peoples R China
[2] Dalian Univ Technol, Sch Architecture & Fine Art, Dalian 116023, Peoples R China
来源：
ENERGIES | 2025年 / 18卷 / 05期
关键词：
urban heat island; local climate zone; field measurement; Shenyang; ENERGY-CONSUMPTION; URBAN; IMPACT; TEMPERATURE; METHODOLOGY; PERFORMANCE; SCHEME; ROME; FORM;
D O I：
10.3390/en18051053
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
The data derived from Local Climate Zone (LCZ) field measurements can contribute to the construction of regional climate datasets with urban heat island (UHI) effects and accurately present urban heat island intensity (UHII) characteristics in different areas, thereby improving the accuracy of building energy consumption simulations. This study focuses on Shenyang, a severe cold-region city, as the research area. By mapping the LCZs in the central city of Shenyang and selecting eight different types of LCZ plots for field temperature measurement, the UHI effect of various LCZs in Shenyang was analyzed. Air temperature and UHII were used to evaluate the UHII characteristics of LCZs under typical meteorological conditions. Additionally, this study investigated the temperature dynamics and heating/cooling rates of each LCZ under typical meteorological days. The results reveal significant differences in UHII characteristics among LCZ types, closely related to their surface structure and land cover characteristics. These findings further validate the effectiveness of the LCZ classification method in severe cold regions. The data obtained in this study can be used as high-precision climate model parameters for urban energy consumption models and building energy efficiency models, thus making simulation results more consistent with local characteristics and enabling more accurate energy consumption predictions.
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页数：22
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