土木工程英文文献及翻译-英语论文

土木工程英文文献及翻译-英语论文
土木工程英文文献及翻译
in Nanjing, China
Zhou Jin, Wu Yezheng *, Yan Gang
Department of Refrigeration and Cryogenic Engineering, School of Energy and Power Engineering, Xi’an Jiao Tong University,
Xi’an 710049, PR China
Received 4 April 2005; accepted 2 October 2005
Available online 1 December 2005
AbstractThe bin method, as one of the well known and simple steady state methods used to predict heating and cooling energy
consumption of buildings, requires reliable and detailed bin data. Since the long term hourly temperature records are not
available in China, there is a lack of bin weather data for study and use. In order to keep the bin method practical in China,
a stochastic model using only the daily maximum and minimum temperatures to generate bin weather data was established
and tested by applying one year of measured hourly ambient temperature data in Nanjing, China. By comparison with the
measured values, the bin weather data generated by the model shows adequate accuracy. This stochastic model can be used
to estimate the bin weather data in areas, especially in China, where the long term hourly temperature records are missing
or not available.
Ó 2005 Elsevier Ltd. All rights reserved.
Keywords: Energy analysis; Stochastic method; Bin data; China
1. Introduction
In the sense of minimizing the life cycle cost of a building, energy analysis plays an important role in devel-
oping an optimum and cost effective design of a heating or an air conditioning system for a building. Several
models are available for estimating energy use in buildings. These models range from simple steady state mod-
els to comprehensive dynamic simulation procedures.
Today, several computer programs, in which the influence of many parameters that are mainly functions
of time are taken into consideration, are available for simulating both buildings and systems and performing
hour by hour energy calculations using hourly weather data. DOE-2, BLAST and TRNSYS are such
* Corresponding author. Tel.: +86 29 8266 8738; fax: +86 29 8266 8725. E-mailaddress:**************(W.Yezheng).
0196-8904/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.enconman.2005.10.006
Nomenclature
Z. Jin et al. / Energy Conversion and Management 47 (2006) 1843–1850 number of days
frequency of normalized hourly ambient temperature
MAPE mean absolute percentage error (%)
number of subintervals into which the interval [0, 1] was equally divided number of normalized temperatures that fall in subinterval
probability density
hourly ambient temperature (°C)
normalized hourly ambient temperature (dimensionless)
weighting factor
Subscripts
calculated value
measured value
max daily maximum
min daily minimum
programs that have gained widespread acceptance as reliable estimation tools. Unfortunately, along with
the increased sophistication of these models, they have also become very complex and tedious to
use [1].
The steady state methods, which are also called single measure methods, require less data and provide
adequate results for simple systems and applications. These methods are appropriate if the utilization of
the building can be considered constant. Among these methods are the degree day and bin data methods.
The degree-day methods are the best known and the simplest methods among the steady state models.
Traditionally, the degree-day method is based on the assumption that on a long term average, the solar
and internal gains will offset the heat loss when the mean daily outdoor temperature is 18.3 °C and that
the energy consumption will be proportional to the difference between 18.3 °C and the mean daily tempera-
ture. The degree-day method can estimate energy consumption very accurately if the building use and the
efficiency of the HVAC equipment are sufficiently constant. However, for many applications, at least one
of the above parameters varies with time. For instance, the efficiency of a heat pump system and HVAC equip-
ment may be affected directly or indirectly by outdoor temperature. In such cases, the bin method can yield
good results for the annual energy consumption if different temperature intervals and time periods are
evaluated separately. In the bin method, the energy consumption is calculated for several values of the outdoor
temperature and multiplied by the number of hours in the temperature interval (bin) centered around that
temperature. Bin data is defined as the number of hours that the ambient temperature was in each of a set
of equally sized intervals of ambient temperature.
In the United States, the necessary bin weather data are available in the literature [2,3]. Some researchers
[4–8] have developed bin weather data for other regions of the world. However, there is a lack of information
in the ASHRAE handbooks concerning the bin weather data required to perform energy calculations in build-
ings in China. The practice of analysis of weather data for the design of HVAC systems and energy consump-
tion predictions in China is quite new. For a long time, only the daily value of meteorological elements, such as
daily maximum, minimum and average temperature, was recorded and available in most meteorological
observations in China, but what was needed to obtain the bin weather data, such as temperature bin data,
were the long term hourly values of air temperature. The study of bin weather data is very limited in China.
Only a few attempts [9,10] in which bin weather data for several cities was given have been found in China.
Obviously, this cannot meet the need for actual use and research. So, there is an urgent need for developing bin
weather data in China. The objective of this paper, therefore, is to study the hourly measured air temperature
distribution and then to establish a model to generate bin weather data for the long term daily temperature
data.2. Data used
Z. Jin et al. / Energy Conversion and Management 47 (2006) 1843–1850 In this paper, to study the hourly ambient temperature variation and to establish and evaluate the model, a
one year long hourly ambient temperature record for Nanjing in 2002 was used in the study. These data are
taken from the Climatological Center of Lukou Airport in Nanjing, which is located in the southeast of China
(latitude 32.0°N, longitude 118.8°E, altitude 9 m).
In addition, in order to create the bin weather data for Nanjing, typical weather year data was needed.
Based on the long term meteorological data from 1961 to 1989 obtained from the China Meteorological
Administration, the typical weather year data for most cities in China has been studied in our former research
[11] by means of the TMY (Typical Meteorological Year) method. The typical weather year for Nanjing is
shown in Table 1. As only daily values of the meteorological elements were recorded and available in China,
the data contained in the typical weather year data was also only daily values. In this study, the daily maxi-
mum and minimum ambient temperature in the typical weather year data for Nanjing was used.
3. Stochastic model to generate bin data
Traditionally, the generation of bin weather data needs long term hourly ambient temperature records.
However, in the generation, the time information, namely the exact time that such a temperature occurred
in a day, was omitted, and only the numerical value of the temperature was used. So, the value of each hourly
ambient temperature can be treated as the independent random variable, and its distribution within the daily
temperature range can be analyzed by means of probability theory.
3.1. Probability distribution of normalized hourly ambient temperature Since the daily maximum and minimum temperatures and temperature range varied day by day, the con-
cept of normalized hourly ambient temperature should be introduced to transform the hourly temperatures in
each day into a uniform scale. The new variable, normalized hourly ambient temperature is defined by
^ ¼ttmintmaxtmin
where ^ may be termed the normalized hourly ambient temperature, tmaxand tminare the daily maximum and
minimum temperatures, respectively, t is the hourly ambient temperature. Obviously, the normalized hourly ambient temperature ^ is a random variable that lies in the interval [0, 1].
To analyze its distribution, the interval [0, 1] can be divided equally into several subintervals, and by means of
the histogram method [12]i
in each subinterval can be calculated by1137
土木工程英文文献及翻译
Based on the one year long hourly ambient temperature data in Nanjing, China, the probability density pi
was calculated for the whole day and the 08:00–20:00 period, where the interval [0, 1] was equally divided into
50 subintervals, namely n equals 50. The results are shown in Fig. 1. According to the discrete probability density data in Fig. 1, the probability density function of ^ can be
obtained by a fitting method. In this study, the quadratic polynomials were used to fit the probability density data, where a, b and c are coefficients. According to the property of the
probability density function, the following equation should be satisfied
As shown in Fig. 1, the probability density curve obtained according to the probability density data points
is also shown. The probability density functions that are fitted are described by
p ¼2:7893^23:1228^ þ 1:6316 for the whole day period
p ¼2:2173^20:1827^ þ 0:3522 for the 08 : 00–20 : 00 period
3.2. The generation of hourly ambient temperature
As stated in the beginning of this paper, the objective of this study is to generate the hourly ambient tem-
perature needed for bin weather data generation in the case that only the daily maximum and minimum tem-
peratures are known. To do this, we can use the obtained probability density function to generate the
normalized hourly ambient temperature and then transform it to hourly temperature. This belongs to the
problem of how to simulate a random variable with a prescribed probability density function and can be done
on a computer by the method described in the literature [13]. For a given probability density function f ð^Þ, if
its distribution function F ð^Þ can be obtained and if u is a random variable with uniform distribution on [0, 1],
then
F, we need only set
As stated above, the probability density function of the normalized ambient temperature was fitted using a
one year long hourly temperature data. Based on the probability density function obtained, the random nor-
malized hourly temperature can be generated. When the daily maximum and minimum temperature are
known, the normalized hourly temperature can be transformed to an actual temperature by the following
equation
When the hourly temperature for a particular period of the day has been generated using the above method,
the bin data can also be obtained. Because the normalized temperature generated using the model in this study
is a random variable, the bin data obtained from each generation shows some difference, but it has much sim-
ilarity. To obtain a stable result of bin data, the generation of the bin data can be performed enough times,
and the bin data can be obtained by averaging the result of each generation. In this paper, 50 generations were
averaged to generate the bin weather data.
Z. Jin et al. / Energy Conversion and Management 47 (2006) 1843–1850 3.4. Methods of model evaluation
The performance of the model was evaluated in terms of the following statistical error test:
土木工程英文文献及翻译
一种产生bin气象数据的随机方法
——中国南京
周晋
摘要：bin方法是一种众所周知且简捷的稳态的计算方法，可以用来预计建筑的冷热能耗。

该方法需要可靠详细的bin数据。

因为在中国长期的逐时温度记录不可用，故没有用来研究和应用的bin天气数据。

为了让bin方法能在中国应用，在中国南京建立并验证了一种随机模型，通过提供日最高和最低气温该随机模型可以产生bin气象数据。

与实测值进行比较，该模型产生的bin气象数据显示出很高的精确性。

在那些长期逐时气温记录丢失或不可用的地区，特别是在中国，可以用该随机模型估算bin气象数据。

关键词：能耗分析；随机模型；Bin数据；中国
1.绪论
为了最大限度减少建筑寿命周期内的费用，能耗分析在发展最适宜和成本更优化的建筑采暖和空调系统设计方面起到了很重要作用。

这些模型涉及从简单稳定模型到复杂动态模拟程序。

现今，一些电脑程序可用于模拟建筑和空调系统。

这些程序使用逐时气象数据进行能耗计算，考虑了许多时间函数参数的影响。

如DOE-2，BLAST and TRNSYS 就属此类程序，它们已经被当作可靠的估算工具得到广泛的认可。

不足之处是，随着模型复杂性的增加，这些程序变得复杂而且使用起来十分冗长乏味。

稳态方法，又称单测量法，只需要很少的数据且能够为简单的系统和应用提供足够的结论数据。

其中有度日数法和bin数据法。

在稳态模型中，度日数法是最知名和最简单的方法。

传统上，度日数法建立在一个长期帄均的假设基础上。

当室外日气温是18.3℃时，建筑日射和内部得热将抵消其热量损失。

并且，能耗与差值（室外帄均气温与18.3℃的差值）成比例。

如果建筑的使用情况与空调系统设备足够稳定，度日数法能够十分准确地估算能
耗。

然而，在许多应用场合下，上述的参数中至少有一个逐时变化。

比如，热泵系统的效率和HAVC设备可能直接或间接受室外气温的影响。

在这种情况下，如果不同温度间隔和时间段分别估算，bin方法可以得到更好的年能耗结果。

在bin 方法中，能耗计算是取几个室外温度进行计算的，并且被这个温度为中心的温度区间的逐时数相乘。

Bin数据定义为：环境温度在各自一系列相等的环境温度区间的小时数。

在美国，必要的bin气象数据可以在文献中得到。

一些研究人员已经为世界上其他地区发展了bin气象数据。

然而，在包涵用于计算能耗的bin气象数据的ASHRAE手册中，没有关于中国的数据。

为用于HAVC系统设计和能耗预算的气象数据进行分析的实践活动在中国还比较新。

在很长的时期内，在中国大部分气象站，只有比如日最高最低和帄均气温数据被记录下来，可以使用，但是为了得到bin气象数据（比如温度bin）而需要的数据是逐时空气温度。

关于bin气象数据的研究在中国相当有限。

在中国仅建立了相当少的试点，在这些试点中，给出了一些城市的bin气象数据。

很明显，这不能满足实际应用和研究的需求。

所以在中国发展bin气象数据的要求十分紧迫。

因此，本文的目的就是研究逐时测量空气温度区间，进而为长期日气温数据建立模型产生bin气象数据。

2.使用到的数据
在本文中，为了研究逐时环境温度的变化以及建立和评价该模型，研究过程中用到了南京2002年一年内的逐时环境温度记录。

这些数据取自南京路口风机场气象站，该气象站位于中国东南部，（北纬32.0℃，东经118.8℃，海拔9m）。

另外，为了在南京创建bin气象数据，典型气象年的数据是必需的。

以中国气象局字1961到1989年间长期的气象数据为依据，在我们先前的研究中，我们用典型气象年方法研究了中国大部分城市的典型气象年数据。

南京典型气象年如表1所示。

因为在中国只有每日的气象参数被记录和可用，故典型气象年包涵的数据也是每日的值。

只这次研究中，用到了南京典型气象年中的最高和最低日环境温度。

3.随机模型计算bin数据
传统上，bin气象数据的产生需要长期的逐时环境温度记录。

然而，在产生过程中，时间信息即某个温度发生的准确时间被忽略了。

仅仅用到了气温的数值，每日的气温分布区间可以用概率论进行分析。

3.1 标准逐时温度概率区间
既然，最高和最低日气温和气温分布范围每天都在变化，故标准逐时环境温度的概念就应该被提出来，它可以将每天的逐时温度化到一个统一的标准形式。

这个新变量，标准逐时环境温度定义为
式中为标准逐时环境温度，和分别是最高和最低日气温，是逐时环境温度。

很明显，标准逐时环境温度的随机变化区间在[0，1]内，为了分析该分布状态，可以用柱状图方法将区间[0，1]划分为若干个相等的子区间，可以用下式计算各个子区间中的概率分布密度。

表一
南京典型气象年
式中是第i个子区间的概率密度；n是子区间的数目；是落在第个子区间的频率。

当周期是一整天时，可以有下式计算：
式中是标准气温落在第 i个子区间的数目；D 是计算日期的天数。

另外，在计算bin气象数据的时间，一天内其他时段（比如：08：00——20：00），常常是必需的。

在这样情况下，可经下式求得：
土木工程英文文献及翻译
图a 表示的是一整天。

图b表示的是08：00-20：00。

根据逐时气温概率密度函数绘制。

以中国南京一年时间内的逐时环境温度为依据，一整天和时段08：00——20：00的概率密度函数被计算出。

结果如图表1所示。

此处区间[0，1]被帄均分成50个子区间，即n等于50。

根据图表1中离散的概率密度数据，概率密度函数可以用拟合的方法得到。

在此次研究中，二次多项式：
可以用来拟合概率密度数据，式中a、b、c是系数。

根据概率密度函数的性质，将有如下方程：
如图表1所示，根据所示概率密度数据点可以得到概率密度曲线。

概率密度函数可以拟合描述成下列关系式
3.2 逐时气温的产生
正如本文开头表述的那样，这项研究的目的是得出逐时环境温度。

当只已知最高日气温和最低日气温时，为了求得bin气象数据。

我们可以通过上述方法求解概率密度函数得到标准的逐时环境温度，进一步可将标准逐时环境温度化成逐时温
度。

这属于怎样模拟一个指定概率密度函数的随机变量的问题。

并且能够通过使用文献[13]提到的方法在电子计算机上处理。

如假设一个概率密度函数，如果的分布函数能得到，并且u是数值在一个特定区间[0，1]内的随机变量时。

那么
得出分布函数。

因此，为了模拟一个值在特定区间F的随机变量，我们只要建立即可。

正如上文讲述的那样，标准环境温度的概率密度函数是用一年时间的逐时温度建立的。

有了已知的概率密度函数做基础，那么随机标准逐时气温就能求出。

当已知日最高气温和最低气温时，可以运用下式把标准逐时温度化成实际温度。

3.3 求解bin数据
通过上述方法可以把一天中特定时段的逐时温度求出，此时，bin数据也可以得到了。

因为在这次研究的模型中使用的标准温度是随机变量，所以世代求解出的bin数据表现出一些差异，但是有很多共同点。

为了得到稳定的bin数据，bin 数据的世代要执行足够多次。

并且去不同世代结果的帄均值做为bin数据。

在此文中，去50个世代结果的帄均值来得出bin天气数据。

3.4 模型的评价方法
模型的性能的评价按照如下统计错误测试。

2002南京bin数据测量和计算值。

图a表示一年中全天的bin数据；图b表示一年中时段8：00-20：00的bin数据；图c表示5月1日-10月31日中全天的bin数据；图d表示5月1日-10月31日中时段8：00-20：00的bin数据；图
e 11月1日-5月1日中全天的bin数据；图f表示11月1日-5月1日时段8：00-20：00的bin数据。

式
中
因为气温的频率分布是一个喇叭形曲线，故可以进行如下设想。

数据中心段是最有意义的，而最大值和最小值反映了异常的天气情况。

因为bin方法是用来计算建筑物能耗的，故集中在中心段的数据是有意义的，中心段数据反映了大部分的能耗。

为了在模型评价中把这个因素考虑进出，在Eq.[11]中一个额外的因数被引入。

4.结论和讨论
4.1模型的确认
基于上述在研究中的模型，以及南京地区2002最高日气温和最低日气温。

两个不同季节（5月1日——10月31日和11月1日——4月30）的 bin天气数据就得到了。

在每个不同季节，一天中不同的时段（全天24小时和一天中08：00——20.：00）都被考虑进出了。

得到的bin天气数据如图表2所示，在图表中，计算和表示了实际的bin数据和统计误差。

如图2所示，本文中建立模型的误差在区间[3.25%，6.5%]内，这样的结果被认为是可以接受的。

尽管这个比较仅仅一年内南京的气温记录为依据，（因此，图表2中的bin数据不能用来计算建筑能耗），
它预示了该研究中建立的模型是切实可行的，并且如果能够得到长期的气温记录，它就可能用来求出bin数据。

4.2 南京的bin数据
在我们以往的研究中，中国大多数城市典型气象年的规范是用TMY方法来研究的。

其中只考虑了最高日气温、最低日气温和帄均环境温度。

以本文中建立的模型以及图表1中南京的典型气象年数据为依据，就可以得到两个不同季节和一天中不同时段的bin气象数据，结果如表2所示。

表2中，在bin数据的产生过程中，气温bin设为2℃。

另外，如果需要求夜晚20：00——08：00的bin数据，可
以通过从整天的bin数据减出时段08：00——20：00的bin数据得到。

5结论
Bin 气象数据对bin方法的应用是重要。

Bin方法能够有效地用来估算建筑的采暖和制冷需求量。

本文建立了一个用以产生bin天气数据的随机模型，在该随机模型中仅需用到最高和最低日气温。

用该随机模型，我们得到了南京地区的bin 数据。

统计评估指标MAPE被用来评估该模型。

因为本文中考虑了运行季节和占有时期，MAPE值在区间[3.25%，6.5%]内。

该模型可以用来估算那些逐时气温数据丢失或不可用的地区的bin气象数据。