更新时间：2010-5-11: 来源：毕业论文

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-mail address: yzwu@mail.xjtu.edu.cn (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

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