• Journal of Bio-Environment Control
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• v.23 no.3
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• pp.174-180
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• 2014

Standard weather data available to greenhouse environmental design are limited in most regions of the country. So, instead of using standard weather data, in order to find the method to build design weather data for greenhouse heating and cooling, design outdoor weather conditions were analyzed and compared by TAC method and frequency analysis using climatological normal and thirty years from 1981 to 2010 hourly weather data provided by KMA and standard weather data provided by KSES. Average TAC values of outdoor temperature, relative humidity and insolation using thirty years hourly weather data showed a good agreement with them using standard weather data. Therefore, in regions which are not available standard weather data, we suggest that design outdoor weather conditions should be analyzed using thirty years hourly weather data. Average of TAC values derived from every year hourly weather data during the whole period can be established as environmental design standards, and also minimum and maximum of them can be used as reference data.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.11
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• pp.752-759
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• 2008

This paper is to make up the climograph using standard weather data of the region of Seoul. It is made up by existed standard weather data of which the subjected region is Seoul in order to easily use work. The factors of weather data are outside air temperature and its absolute humidity, total solar radiation, amount of clouds, wind direction, and wind velocity. The standard weather data are verified by comparing with values of the existed degree day method. As the result of their verification, the difference of the data showed less than 3% each other. And, reliability of standard weather data is thought to be same as those of degree day.

• Journal of Bio-Environment Control
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• v.23 no.3
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• pp.192-198
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• 2014

To establish the design criteria for seasonal heating load calculation in greenhouses, standard weather data are required. However, they are being provided only at seven regions in Korea. So, instead of using standard weather data, in order to find the method to build design weather data for seasonal heating load calculation, heating degree-hour and heating degree-day were analyzed and compared by methods of fundamental equation, Mihara's equation and modified Mihara's equation using normal and thirty years from 1981 to 2010 hourly weather data provided by KMA and standard weather data provided by KSES. Average heating degree-hours calculated by fundamental equation using thirty years hourly weather data showed a good agreement with them using standard weather data. The 24 times of heating degree-day showed relatively big differences with heating degree-hour at the low setting temperature. Therefore, the heating degree-hour was considered more appropriate method to estimate the seasonal heating load. And to conclude, in regions which are not available standard weather data, we suggest that design weather data should be analyzed using thirty years hourly weather data. Average of heating degree-hours derived from every year hourly weather data during the whole period can be established as environmental design standards, and also minimum and maximum of them can be used as reference data for energy estimation.

• Journal of the Korean Solar Energy Society
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• v.34 no.1
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• pp.8-18
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• 2014

Building simulation is used in a variety of sectors. In its early years, building simulation was mainly used in the design phase of a building for basic functions. Recently, however, it has become increasingly important during the operating phase, for commissioning and facility management. Most building simulation tools are used to estimate the thermal environment and energy consumption performance, and hence, they require the inputting of hourly weather data. A building simulation used for prediction should take into account the use of standard weather data. Weather data, which is used as input for a building simulation, plays a crucial role in the prediction performance, and hence, the selection of appropriate weather data is considered highly important. The present study proposed a technique for generating real-time weather data files, as opposed to the standard weather data files, which are required for running the building simulation. The forecasted weather elements provided by the Korea Meteorological Administration (KMA), the elements produced by the calculations, those utilizing the built-in functions of Energy Plus, and those that use standard values are combined for hourly input. The real-time weather data files generated using the technique proposed in the present study have been validated to compare with measured data and simulated data via EnergyPlus. The results of the present study are expected to increase the prediction accuracy of building control simulation results in the future.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.14 no.2
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• pp.125-137
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• 1985

Standard weather data for Seoul has been developed for use in computer calculations for energy requirements, and the 8760 sequential hourly values for seven weather elements have been placed in magnetic tape and cards. Applying the method determining average month, developed by Japanese Society of Heating, Air-Conditioning and Sanitary Engineers, the standard year data have been selected from the monthly average values for three weather elements during the 10year period of 1971 through 1980. The followings are obtained. 1. The Test Reference Year, consisting of 12 months chosen from different calendar years, has been determined, and TRY tape which contains seven weather elements has been prepared. 2. The Typical Weather Year, which means a year close to the average value, is the year of 1978 during the above 10 year period. 3. During the period, Winter Season needs the maximum heating load is from Dec. 1976 to Mar. 1977 and Summer Season needs the maximum cool ins load is from Jun. to Sep, 1978.

• International Journal of Air-Conditioning and Refrigeration
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• v.11 no.4
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• pp.199-208
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• 2003

Standard temperature and absolute humidity weather data correlations of Seoul for dynamic energy simulation have been developed regressing the measured data compiled by the Korea Meteorological Adminstration during a l0-year period from 1991 to 2000. The mathematical equations can generate consistent daily and yearly variations of outdoor weather data unlike the measured data which may show abnormal behavior. Considering that each hour of the day follows a certain yearly pattern, 24 correlations are developed for each hour of the day. The derived simple mathematical equations can be used for estimating outdoor temperature and humidity conditions for any arbitrary time of the year.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.11
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• pp.897-906
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• 2002

Standard temperature and absolute humidity weather correlations of Seoul for dynamic energy simulation have been developed regressing the measured data compiled by the Korea Meteorological Adminstration during a 10-year period from 1991 to 2000. The mathematical equations can generate the daily and yearly variations of outdoor weather data with consistency unlike the measured data which may show abnormal behavior, Considering that each hour of the day follows a certain yearly pattern, the correlations are developed for each hour. The derived 24 simple mathematical equations can be used for estimating outdoor temperature and humidity conditions for any arbitrary time of the year.

• Journal of the Korean Solar Energy Society
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• v.29 no.3
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• pp.66-72
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• 2009

As 'Low Carbon Green Building' is highly required, programs to evaluate building performance are actively and commonly used. For most of these programs, dynamic responses of buildings against external weather changes are very important. In order to simulate the programs, weather data of each region must be properly entered to estimate accurate amount of building energy consumption. To this end, the existing weather data and weather data of KSES were compared and analyzed to find out how weather changes. Energy load of Korea's standard houses was also analyzed based on this data. As a result, data corresponding to June ${\sim}$ September when cooling is supplied shows 23% of average increase with 30% of peak increase(June). On the other hand, data corresponding to November ${\sim}$ February when heating is supplied shows 29% of average decrease with 34% of peak decrease(November). Increase in cooling load and decrease in heating load in the above data comparison/analysis show that KSES 2009 data reflects increase in average temperature caused by global warming unlike the existing data. Increase in dry-bulb temperature depending on weather change of standard houses increases cooling load by 17% and decreases heating load by 36%

• Atmosphere
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• v.20 no.2
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• pp.153-160
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• 2010

In this paper, an attempt has been made to build and test self-configuring weather sensor networks and internet based observation system to gather atmospheric data. The aim is to provide integrated or real-time weather information in standard form using network data access protocol. This system was successfully developed to record weather information both digital as well as visual using sensor network and web-enabled surveillance cameras. These data were transformed by network based data access protocol to access and utilize for public domain. The competed system has been successfully utilized to monitor different types of weather. The results show that this is one of the most useful weather monitoring system.

• Water Engineering Research
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• v.4 no.3
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• pp.111-126
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• 2003

Meteorological data are often needed to evaluate the long-term effects of proposed hydrologic changes. The evaluation is frequently undertaken using deterministic mathematical models that require daily weather data as input including precipitation amount, maximum and minimum temperature, relative humidity, solar radiation and wind speed. Stochastic generation of the required weather data offers alternative to the use of observed weather records. The precipitation is modeled by a Markov Chain-exponential model. The other variables are generated by multivariate model with means and standard deviations of the variables conditioned on the wet or dry status of the day as determined by the precipitation model. Ultimately, the objective of this paper is to compare Richardson's model and the improved weather generation model in their ability to provide daily weather data for the crop model to study potential impacts of climate change on the irrigation needs and crop yield. However this paper does not refer to the improved weather generation model and the crop model. The new weather generation model improved will be introduced in the Journal of KWRA.