气候变化对空调室外计算参数的影响及确定方法研究
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摘要
室外空气设计计算参数是室外气候和建筑暖通设计的桥梁，对暖通空调负荷和相关设备选型具有非常重要的作用。我国的室外设计计算参数确定方法制定至今已有30多年，未曾有本质上的变化。当前，气候变暖已成为全球性的共识，围护结构保温性能也有很大的提高。在此背景下，有必要针对气候变暖对计算参数本身及确定方法的影响进行研究，并进一步结合新的气候特点和建筑特点，探究新的确定方法。
本文利用气候学、数理统计和极值统计等相关交叉学科知识，在相关基金的支持下以天津市1960-2010年共51年的气象观测数据为基础数据，对原有确定方法的适应性和各国方法在中国的适应性进行了探讨，计算分析了气候变化的显著性以及在气候变化情况下各室外计算参数的数值变化，对室外计算参数统计时长的长短限值进行了研究，并利用平稳时间序列下的赋权对最优时长进行了较为深入的证明分析，最后建立了一套新的充分考虑气候特点和建筑需要的夏季空调室外设计参数确定方法体系，主要研究内容及结果如下：
①通过对比中英美方法下的同一地区室外设计计算参数，发现照搬国外设计参数确定方法存在一定不合理性，通过计算气候变化情况下，湿球温度和露点温度的不保证时长和变化趋势，发现现有确定方法下的状态点不能适应气候变化，也不能良好的反应干湿度的分布。
②利用M-K检验，证明空调采暖季天津地区平均干球温度、日较差均呈显著性变化，极端高温事件90年代后频数明显增加。计算气候变化下各室外计算参数发现，天津地区采暖室外计算温度每隔14年、冬季空调室外计算温度每隔20年、夏季空调室外计算温度每隔20年就会提高1℃。
③在标准差为定值条件下，经过计算各设计参数的统计年限并不完全相同，在标准差最大为1度、最小为0.1度的条件下，计算天津地区各参数的统一限值上下限分别为43年和13年。
④建立了一套新的方法证明考虑气候变化下最优统计年限：证明高温气象数据总体服从广义帕累托分布(GPD)，并以天津地区为例进行了模型拟合，经模型诊断良好。在考虑气候变化的前提下，对气象数据样本引入时间维度，证明气温必符合平稳时间序列，定义描述对今影响程度的气象影响因子，计算影响因子并对不同年限组合序列赋以权值，与总体进行比较，最后得出考虑气候变化下的最优时长。以天津为例进行案例分析，发现天津地区的最优时长为17年。
⑤对固定不保证50小时存在的问题进行了计算，并提出了一种新方法来确定不保证率。采用阀值温度均值函数（SMEF）来初选设计温度，并按照一定连续发生事件处理数据样本，去掉建筑围护结构可抵御的事件，再次选取设计温度，如此反复迭代直至两次选取温度相差在0.1度之内停止，最后得到设计温度。以天津地区为例，空调室外设计干球温度按新方法计算为33.5摄氏度。
⑥在前人的基础上，扩展得到了一种考虑同时发生的空调室外设计参数的方法，根据某地区气象数据，得到干湿球温度的二维联合概率分布，在一定不保证率的条件下，以a作为代表，考虑建筑热物性对于建筑负荷特性的影响，确定出斜率为a的等负荷线的干湿球温度组合，同时为了保留空调室外设计参数的代表性，将所得到的干湿球温度求期望，从而最终获得在一定a时室外的设计计算参数。文章在假设a=2、a=1和a=0.5的情况下分别与ASHRAE、CIBSE所给出的设计参数进行对比，得到的结果表明新方法与ASHRAE中给出的湿球温度差别较大。
本文还将第四章和第五章方法结合形成一套空调室外设计计算参数新的综合确定方法。新方法条件下，天津地区的空调室外设计计算干球温度为32.6摄氏度，湿球温度为26.1摄氏度。同时，基于不同的a值，本文给出了室外设计参数与不保证率的关系曲线供设计时选用。
Outdoor design parameter for AC system is the bridge between climate andHVAC design. It plays very important role in HVAC load calculation and equipmentselection。It has been30years since the determination method was firstly established,which hasn‘t been essentially changed since then。At present, global warming hasbecome a globalconsensus, hence in this context, it is necessary to study the impact ofclimate change and determination method of design parameters. Further integration ofcharacteristics of new climate and architectural features will be taken intoconsideration to build up a new system of generating outdoor design parameters.
In this paper,climate science,mathematical statistics, extreme value statistics andsome other interdisciplinary knowledge are used. Meanwhile, in then support ofrelated fund and based on the hourly weather data of Tianjin from1960to2010,theadaptability of China National Standard and other determination method arediscussed.Related calculationhelps analyze the significance of climate change andrespective numerical difference of outdoor design parameters. This paper conductresearch on statistical length limitations used for calculation, andutilize determiningweights under stationary time series to analyze and prove the optimal length indepth.Finally this paper build up a whole new system fully considered thecharacteristics of local climate and architectural features to determine the optimaloutdoor design parameters.
Main Contents and Results as follows:
①Comparing the difference of outdoor design parameters based on ChinaNational Standard,ASHRAE and CIBSE, we find that there is a certain irrationalityto process original method with new data. Further calculation on climate change,duration trends of risk levelshows that existing method cannot agree with the climatechange as well as the distribution of dry-and wet-bulb temperature.
②By utilizing M-K Test，it is proved that the average dry bulb temperature,dailytemperature difference changed significantly in air conditioning or heatingperiod.Besides, the occurrence frequency of extreme high temperature increased quitemuch.Moreover,by calculating outdoor design temperature in case of climate change, it is found that the outdoor design temperature raises1oC every14years forheating/20years for space heating/20years for cooling.
③Under the circumstance that the standard deviation remains constant，thestatistical life of each design parameters are not same as each other.Given that thestandard deviation ranges from0.1to1, the higher limit and lower limit of designparameters are43years and13years respectively.
④This paper established a new system to prove the best statistical lifeconsidering climate change：A new system proved that the high temperature weatherdata obey the Generalized Pareto Distribution (GPD), and the weather data of Tianjin,for example, fits the model diagnostics excellently. Considering the impact ofclimatechange, a time dimension is introduced to prove that temperature shall comply withthe stationary time series. We also define a meteorological impact factor which iscalculated for empowering value of different combination of vary statisticalperiod.After comparisonwith theoriginal data, an optimal duration is foundconsidering the impact of climate change.For example, Tianjin‘s optimal duration is17years.
⑤A series of calculation are conducted to find the problem of non-guaranteedduration length on certain50hrs.，and a new method is promoted to determine the ratewhich the real value exceeds the design parameters.We primarily choose the designtemperatures by utilizing SMEF and then process data in accordance with certaincontinuousoccurrence events, and then we reselect the design parameters removingthose events that the building envelope can resist.Keep doing so iteratively until thedifference of two selected temperature smaller than0.1degree. For Tianjin City,thedesign temperature is33.5degree.
⑥In the basis of the predecessors, an expansion has been established consideringthe coincidence of outdoor design parameters.Based on the hourly weather data ofcertain region,we can get the joint probability distribution of dry-and wet-bulbtemperature. Once the risk level and the variable a,which presents thermal andphysical properties of building, are determined, we could thus generate an assembly,which contains a series sets of dry-and wet-bulb temperaturethat leads to equalbuilding load. And the expectation of the assembly is the design parameters ofoutdoor air. This paper assumes3circumstances,that a=2,a=1and a=0.5,and thecorrespondingresults show that the wet-bulb temperature generated by newapproachdiffers much from ASHRAE.
This Paper also combine the approaches promoted in Chapter4and Chapter5.Via the new approach, the outdoor design temperature of Tianjin is: DBT-33.5degree，WBT-26.3degree. At the meantime, based on different a‘value, this paperalso generates X-Y charts of outdoor design temperature and risk level.
本文利用气候学、数理统计和极值统计等相关交叉学科知识，在相关基金的支持下以天津市1960-2010年共51年的气象观测数据为基础数据，对原有确定方法的适应性和各国方法在中国的适应性进行了探讨，计算分析了气候变化的显著性以及在气候变化情况下各室外计算参数的数值变化，对室外计算参数统计时长的长短限值进行了研究，并利用平稳时间序列下的赋权对最优时长进行了较为深入的证明分析，最后建立了一套新的充分考虑气候特点和建筑需要的夏季空调室外设计参数确定方法体系，主要研究内容及结果如下：
①通过对比中英美方法下的同一地区室外设计计算参数，发现照搬国外设计参数确定方法存在一定不合理性，通过计算气候变化情况下，湿球温度和露点温度的不保证时长和变化趋势，发现现有确定方法下的状态点不能适应气候变化，也不能良好的反应干湿度的分布。
②利用M-K检验，证明空调采暖季天津地区平均干球温度、日较差均呈显著性变化，极端高温事件90年代后频数明显增加。计算气候变化下各室外计算参数发现，天津地区采暖室外计算温度每隔14年、冬季空调室外计算温度每隔20年、夏季空调室外计算温度每隔20年就会提高1℃。
③在标准差为定值条件下，经过计算各设计参数的统计年限并不完全相同，在标准差最大为1度、最小为0.1度的条件下，计算天津地区各参数的统一限值上下限分别为43年和13年。
④建立了一套新的方法证明考虑气候变化下最优统计年限：证明高温气象数据总体服从广义帕累托分布(GPD)，并以天津地区为例进行了模型拟合，经模型诊断良好。在考虑气候变化的前提下，对气象数据样本引入时间维度，证明气温必符合平稳时间序列，定义描述对今影响程度的气象影响因子，计算影响因子并对不同年限组合序列赋以权值，与总体进行比较，最后得出考虑气候变化下的最优时长。以天津为例进行案例分析，发现天津地区的最优时长为17年。
⑤对固定不保证50小时存在的问题进行了计算，并提出了一种新方法来确定不保证率。采用阀值温度均值函数（SMEF）来初选设计温度，并按照一定连续发生事件处理数据样本，去掉建筑围护结构可抵御的事件，再次选取设计温度，如此反复迭代直至两次选取温度相差在0.1度之内停止，最后得到设计温度。以天津地区为例，空调室外设计干球温度按新方法计算为33.5摄氏度。
⑥在前人的基础上，扩展得到了一种考虑同时发生的空调室外设计参数的方法，根据某地区气象数据，得到干湿球温度的二维联合概率分布，在一定不保证率的条件下，以a作为代表，考虑建筑热物性对于建筑负荷特性的影响，确定出斜率为a的等负荷线的干湿球温度组合，同时为了保留空调室外设计参数的代表性，将所得到的干湿球温度求期望，从而最终获得在一定a时室外的设计计算参数。文章在假设a=2、a=1和a=0.5的情况下分别与ASHRAE、CIBSE所给出的设计参数进行对比，得到的结果表明新方法与ASHRAE中给出的湿球温度差别较大。
本文还将第四章和第五章方法结合形成一套空调室外设计计算参数新的综合确定方法。新方法条件下，天津地区的空调室外设计计算干球温度为32.6摄氏度，湿球温度为26.1摄氏度。同时，基于不同的a值，本文给出了室外设计参数与不保证率的关系曲线供设计时选用。
Outdoor design parameter for AC system is the bridge between climate andHVAC design. It plays very important role in HVAC load calculation and equipmentselection。It has been30years since the determination method was firstly established,which hasn‘t been essentially changed since then。At present, global warming hasbecome a globalconsensus, hence in this context, it is necessary to study the impact ofclimate change and determination method of design parameters. Further integration ofcharacteristics of new climate and architectural features will be taken intoconsideration to build up a new system of generating outdoor design parameters.
In this paper,climate science,mathematical statistics, extreme value statistics andsome other interdisciplinary knowledge are used. Meanwhile, in then support ofrelated fund and based on the hourly weather data of Tianjin from1960to2010,theadaptability of China National Standard and other determination method arediscussed.Related calculationhelps analyze the significance of climate change andrespective numerical difference of outdoor design parameters. This paper conductresearch on statistical length limitations used for calculation, andutilize determiningweights under stationary time series to analyze and prove the optimal length indepth.Finally this paper build up a whole new system fully considered thecharacteristics of local climate and architectural features to determine the optimaloutdoor design parameters.
Main Contents and Results as follows:
①Comparing the difference of outdoor design parameters based on ChinaNational Standard,ASHRAE and CIBSE, we find that there is a certain irrationalityto process original method with new data. Further calculation on climate change,duration trends of risk levelshows that existing method cannot agree with the climatechange as well as the distribution of dry-and wet-bulb temperature.
②By utilizing M-K Test，it is proved that the average dry bulb temperature,dailytemperature difference changed significantly in air conditioning or heatingperiod.Besides, the occurrence frequency of extreme high temperature increased quitemuch.Moreover,by calculating outdoor design temperature in case of climate change, it is found that the outdoor design temperature raises1oC every14years forheating/20years for space heating/20years for cooling.
③Under the circumstance that the standard deviation remains constant，thestatistical life of each design parameters are not same as each other.Given that thestandard deviation ranges from0.1to1, the higher limit and lower limit of designparameters are43years and13years respectively.
④This paper established a new system to prove the best statistical lifeconsidering climate change：A new system proved that the high temperature weatherdata obey the Generalized Pareto Distribution (GPD), and the weather data of Tianjin,for example, fits the model diagnostics excellently. Considering the impact ofclimatechange, a time dimension is introduced to prove that temperature shall comply withthe stationary time series. We also define a meteorological impact factor which iscalculated for empowering value of different combination of vary statisticalperiod.After comparisonwith theoriginal data, an optimal duration is foundconsidering the impact of climate change.For example, Tianjin‘s optimal duration is17years.
⑤A series of calculation are conducted to find the problem of non-guaranteedduration length on certain50hrs.，and a new method is promoted to determine the ratewhich the real value exceeds the design parameters.We primarily choose the designtemperatures by utilizing SMEF and then process data in accordance with certaincontinuousoccurrence events, and then we reselect the design parameters removingthose events that the building envelope can resist.Keep doing so iteratively until thedifference of two selected temperature smaller than0.1degree. For Tianjin City,thedesign temperature is33.5degree.
⑥In the basis of the predecessors, an expansion has been established consideringthe coincidence of outdoor design parameters.Based on the hourly weather data ofcertain region,we can get the joint probability distribution of dry-and wet-bulbtemperature. Once the risk level and the variable a,which presents thermal andphysical properties of building, are determined, we could thus generate an assembly,which contains a series sets of dry-and wet-bulb temperaturethat leads to equalbuilding load. And the expectation of the assembly is the design parameters ofoutdoor air. This paper assumes3circumstances,that a=2,a=1and a=0.5,and thecorrespondingresults show that the wet-bulb temperature generated by newapproachdiffers much from ASHRAE.
This Paper also combine the approaches promoted in Chapter4and Chapter5.Via the new approach, the outdoor design temperature of Tianjin is: DBT-33.5degree，WBT-26.3degree. At the meantime, based on different a‘value, this paperalso generates X-Y charts of outdoor design temperature and risk level.
引文
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