西安建筑科技大学学报(自然科学版)

气候变化日益加剧导致全球变暖、极端事件的频繁发生,极大地影响了建筑能耗.目前,在建筑设计前期,用于建筑能耗模拟的气象参数主要是代表平均状况的典型气象年,无法体现极端天气状况下的建筑耗能情况,因此,生成用于模拟极端天气下建筑能耗的气象年十分必要.对比了几种已有的反映极端天气气象年的优缺点,提出了一种新的用于建筑极端能耗模拟的气象年生成方法,该方法不仅考虑了多种气象参数对建筑耗能的影响,而且使用动态阈值计算多参数的极端强度和出现时长,综合评价各参数超过阈值的时长和强度的得分,生成建筑极端能耗气象年.通过对比不同气象年的建筑能耗模拟结果来验证新方法的优越性.结果显示:采用建筑极端能耗气象年作为室外气象计算参数,对极端天气状况下建筑极值能耗模拟的准确性优于极端气象年、典型冷年及典型热年数据,能够很好模拟出极端天气状况下的建筑能耗.同时,验证了通过组合使用典型气象年和建筑极端能耗气象年数据,可模拟出建筑的平均能耗以及极端天气状况下的极值能耗,组合数据集在不损失模拟结果全面性的前提下减少了模拟次数.

The increasing climate change leads to global warming and the frequent occurrence of extreme events, which greatly affects the energy consumption of buildings. At present, in the early stage of building design, the meteorological parameters used for building energy consumption simulation are mainly typical meteorological years(TMY)that represent average conditions, which can't reflect building energy consumption under extreme weather conditions. Therefore, it is necessary to generate meteorological years for building energy consumption simulation under extreme weather conditions. In this paper, the advantages and disadvantages of several existing extreme weather meteorological years are compared, and a new extreme meteorological year generation method for building energy consumption simulation is proposed. This method not only considers the influence of various meteorological parameters on building energy consumption, but also uses the dynamic threshold to calculate the extreme intensity and occurrence time of multi-parameters, and comprehensively evaluates the score of each parameter exceeding the threshold time and intensity to generate the building extreme energy consumption meteorological year. The superiority of the new method is verified by comparing the simulation results of building energy consumption in different meteorological years. The results show that: by using the building extreme energy consumption meteorological year as the outdoor meteorological calculation parameter, the accuracy of building extreme energy consumption simulation under extreme weather conditions is better than that of EMY, THY and TCY data, and can well simulate the building energy consumption under extreme weather conditions. At the same time, this study verifies that the average energy consumption of buildings and the extreme energy consumption under extreme weather conditions can be simulated by combining the typical meteorological year and the extreme energy consumption meteorological year data of buildings. The combined data set reduces the number of simulations without losing the comprehensiveness of the simulation results.

引言
1 方法
2 结果与分析
3 结论

表1 各热工设计分区代表城市气象台站信息<br/>Tab.1 Information of meteorological stations in representative cities of each thermal design zone

表1 各热工设计分区代表城市气象台站信息
Tab.1 Information of meteorological stations in representative cities of each thermal design zone

表2 TMY生成方法中各参数权重取值<br/>Tab.2 Weight values of each parameter in TMY generation method

表2 TMY生成方法中各参数权重取值
Tab.2 Weight values of each parameter in TMY generation method

图1 技术路线图
Fig.1 Technology Roadmap

表3 建筑极端能耗气象年生成气象参数权重<br/>Tab.3 Generation of parameter weights in Meteorological year of extreme Energy consumption of buildings

表3 建筑极端能耗气象年生成气象参数权重
Tab.3 Generation of parameter weights in Meteorological year of extreme Energy consumption of buildings

表4 各城市建筑极端能耗气象年选取结果<br/>Tab.4 Selection results of meteorological year of extreme energy consumption of buildings in each city

表4 各城市建筑极端能耗气象年选取结果
Tab.4 Selection results of meteorological year of extreme energy consumption of buildings in each city

表5 建筑基本参数设置表<br/>Tab.5 Setting table of basic building parameters

表5 建筑基本参数设置表
Tab.5 Setting table of basic building parameters

图2 哈尔滨能耗值<br/>Fig.2 Energy consumption value in Harbin

图2 哈尔滨能耗值
Fig.2 Energy consumption value in Harbin

图3 北京能耗模拟值<br/>Fig.3 Energy consumption value in Beijing

图3 北京能耗模拟值
Fig.3 Energy consumption value in Beijing

图4 合肥能耗模拟值<br/>Fig.4 Energy consumption value in Hefei

图4 合肥能耗模拟值
Fig.4 Energy consumption value in Hefei

图5 海口能耗模拟值<br/>Fig.5 Energy consumption value in Haikou

图5 海口能耗模拟值
Fig.5 Energy consumption value in Haikou

图6 乌鲁木齐能耗模拟值<br/>Fig.6 Energy consumption value in Urumqi

图6 乌鲁木齐能耗模拟值
Fig.6 Energy consumption value in Urumqi

表6 历史年能耗与TMY能耗相差20%以上的百分比<br/>Tab.6 Percentage difference of more than 20% between energy consumption and TMY energy consumption in historical years

表6 历史年能耗与TMY能耗相差20%以上的百分比
Tab.6 Percentage difference of more than 20% between energy consumption and TMY energy consumption in historical years

表7 各气象年能耗模拟年值(kWh/m2)<br/>Tab.7 Simulated annual energy consumption of each meteorological year

表7 各气象年能耗模拟年值(kWh/m2)
Tab.7 Simulated annual energy consumption of each meteorological year

图7 TMY与历史年能耗值<br/>Fig.7 TMY and historical annual energy consumption

图7 TMY与历史年能耗值
Fig.7 TMY and historical annual energy consumption

表8 建筑极端能耗气象年能耗值涵盖范围<br/>Tab.8 Coverage of energy consumption of meteorological year of extreme energy consumption of buildings

表8 建筑极端能耗气象年能耗值涵盖范围
Tab.8 Coverage of energy consumption of meteorological year of extreme energy consumption of buildings

图8 建筑极端能耗气象年和历史年能耗值<br/>Fig.8 Energy consumption of meteorological year of extreme energy consumption of buildings and historical years

图8 建筑极端能耗气象年和历史年能耗值
Fig.8 Energy consumption of meteorological year of extreme energy consumption of buildings and historical years

图9 TMY、建筑极端能耗气象年及历史年能耗值<br/>Fig.9 Energy consumption of TMY and Meteorological year of extreme Energy consumption of buildings and historical year

图9 TMY、建筑极端能耗气象年及历史年能耗值
Fig.9 Energy consumption of TMY and Meteorological year of extreme Energy consumption of buildings and historical year

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备注

引言

1 方法

2 结果与分析

3 结论

期刊信息