Development and application of a weather data service client for preparation of weather input files to a crop model

Lee, C.K.,Kim, J.,Kim, K.S. Elsevier 2015 Computers and electronics in agriculture Vol.114 No.-

Crop yield prediction has been made using a crop growth model that relies on four categories of input data including soil, crop, management, and weather. Most crop models are single column models, which require individual weather inputs for each site of interest. The objectives of this study were to develop a weather data service client that prepares weather input files for a crop growth model and to examine its application to yield prediction at a national scale. The weather data service client downloads daily weather data from the web-based weather data service portal operated by the Korea Meteorological Administration (KMA). The client also prepares weather input files for the ORYZA 2000 model at minimum effort. In total, 4950 input files were prepared to predict rice yield in 2011 and 2012 using the weather data service client. To prepare nearly 5000 weather input files, it would take more than a month for a skilled person to download weather data from the KMA database and to reorganize those data to the input data format for the ORYZA 2000 model manually. Using the weather data service client, several hours were enough to prepare all the input files without error associated with manual preparation as well as with minimum effort and labor.

A middleware platform for the validation and utilization of short-term weather forecast data for office buildings

Lee, Junghun,Lee, Sungjin,Kim, Jonghun,Song, Doosam,Jeong, Hakgeun Elsevier 2017 Energy and buildings Vol.149 No.-

<P><B>Abstract</B></P> <P>Simulations are widely used to calculate the thermal environment and energy consumption of buildings. The results of these simulations are affected by weather data, thus making the selection of appropriate weather data essential. Typical weather data are used to analyze a building’s thermal performance, but this is not appropriate for analyzing the performance of an actual building controlled by a building energy management system, which responds to real-time weather conditions. Such a building needs short-term weather forecast data to calculate the energy consumption. However, an evaluation of the validity of weather forecast data has not been performed. This study quantitatively analyzes the validation of real-time weather forecast data. A middleware platform was developed to combine weather forecast data and the EnergyPlus software using Grasshopper software. In addition, weather forecast data and actual weather data were compared to evaluate the forecast validation and a predictive control method using weather forecast data was devised. The application of real-time control was found to reduce the electricity costs incurred for cooling by 10% relative to there being no control, and by 2% relative to fixed temperature control.</P>

실시간 빌딩 시뮬레이션을 위한 예측 기상 기반의 기상 데이터 파일 작성 기법

곽영훈(Kwak Young-Hoon),정용우(Jeong Yong-Woo),한혜심(Han Hey-Sim),장철용(Jang Cheol-Yong),허정호(Huh Jung-Ho) 한국태양에너지학회 2014 한국태양에너지학회 논문집 Vol.34 No.1

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 EnergyPlus, 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.

온실의 냉난방부하 산정을 위한 외부기상자료 비교분석

남상운,신현호,서동욱 (사) 한국생물환경조절학회 2014 시설원예‧식물공장 Vol.23 No.3

기상청에서 제공하는 전체 기상자료와 평년값 자료 및태양에너지학회에서 제공하는 표준기상데이터를 이용하여 위험률별 난방설계용 외기온과 상대습도, 냉방설계용건구온도, 습구온도 및 일사량 자료를 분석하였다. 표준기상데이터는 평균에 가장 가까운 대표성을 갖는 1개년의 데이터로 가공 처리한 매시간별 기상자료이고, 평년값 자료는 30년(1981~2010년) 평균 일별 기상자료이며,전체 기상자료는 평년값과 동일기간의 매시간별 기상자료이다. 일부 분석방법은 평년값 자료를 이용하는 경우도 있지만 대부분 매시간별 기상자료가 필요하고 대표성을 갖기 위해서는 표준기상데이터를 이용하는 것이 가장적합하다. 그러나 현재 표준기상데이터는 서울과 6대 광역시 등 7개 지역만 제공되고 있기 때문에 전국을 커버할 수 있는 지역별 온실 환경설계용 기상자료의 구축에는 전체 기상자료를 이용할 수밖에 없다. 따라서 본 연구에서는 표준기상데이터가 제공되고 있는 7개 지역을대상으로 전체 기상자료 및 평년값 자료를 이용한 방법으로 분석하여 표준기상데이터로 구한 값과 비교 검토하였다. 위험률별 설계 기상자료는 전체 기상자료를 이용하여 구한 평균값이 표준기상데이터로 구한 결과와 잘일치하였다. 따라서 표준기상데이터가 없는 지역의 위험률별 설계용 기상자료는 전체 기상자료를 이용하여 구하고, 전체자료 기간의 평균값을 온실의 환경설계 기준으로 사용하며, 최대값과 최소값을 제공함으로써 참고자료로 활용할 수 있도록 하는 것이 합리적인 방법으로 판단된다. 최근의 기후변화 문제로 냉난방 설계기온의 지속적인 변화가 예상되므로 이에 대응하기 위해서는 전문가들로 구성된 온실설계위원회와 같은 기구의 설치가 절실히 요청된다. 위원회에서는 자료기간 및 분석방법에대한 기준을 설정하고, 최소한 10년 간격의 정기적인 검토를 통하여 기상자료를 분석하고 온실설계기준 및 가이드라인을 제공할 필요가 있다. Standard weather data available to greenhouse environmental design are limited in most regions of thecountry. So, instead of using standard weather data, in order to find the method to build design weather data forgreenhouse heating and cooling, design outdoor weather conditions were analyzed and compared by TAC methodand frequency analysis using climatological normal and thirty years from 1981 to 2010 hourly weather data providedby KMA and standard weather data provided by KSES. Average TAC values of outdoor temperature, relativehumidity and insolation using thirty years hourly weather data showed a good agreement with them using standardweather data. Therefore, in regions which are not available standard weather data, we suggest that design outdoorweather conditions should be analyzed using thirty years hourly weather data. Average of TAC values derived fromevery year hourly weather data during the whole period can be established as environmental design standards, andalso minimum and maximum of them can be used as reference data.

건물 에너지 성능 분석을 위한 로컬 기상 데이터 수집 방안

양원영,박상구,문현준 한국생활환경학회 2017 한국생활환경학회지 Vol.24 No.6

To date, typical meteorological data from nearby metropolises have been used for building energy performanceanalysis. It has the disadvantage that it could not accurately reflect the local weather in the site, but it isadvantageous to use the weather data in general without the local weather station of each building site. In this study,we compared the nearby weather data from the KMA(Korea Meteorological Agency) and local predicted data by theWRF(Weather Research and Forecasting Model) with observed weather data from a local weather station(37.320221N, 127.125430 E). The accuracy of dry bulb temperature, relative humidity, and solar radiation were analyzed. Theadvantage of the WRF method was that weather data could be simulated at a specific point, where the building waslocated. The RMSE and MBE of the air temperature and relative humidity were lower with the nearby KMA observeddata. The WRF predicted data tended to underestimate the temperature. The nearby KMA observed data tended tounderestimate the solar radiation. In general, the local solar radiation could not be replaced by nearby observed dataor the predicted data.

기상 및 건축물 성능 정보의 효율적 시각화를 위한 동적 그래픽 표현기법을 활용한 계량 습공기선도 프로그램의 개발

오기환(Oh, Kie-Whan) 한국건축친환경설비학회 2014 한국건축친환경설비학회 논문집 Vol.8 No.3

The purpose of this paper is to develop an effective graphical presentation method which can be applied by an improved weather data display program in visualization of weather data as well as building energy analysis data. Weather data, either measured or calculated for a site, is critically important for the accurate estimation of a building’s energy use. Various methods have been developed to collect and format or pack weather data to make the appropriate weather data files to be used for specific building energy simulation programs. However, most of the previous efforts have mainly focused on the numerical accuracy and reliability of the weather-formatting routines. Therefore, an effective and easy-to-use animated graphical psychrometric-chart program has been developed to visualize the characteristics of weather information and their possible impacts on environment and energy use of the simulated building. This research shows an improved psychrometric chart introducing color schemes and animations that improve visualizing the characteristics of weather data and its impacts on building design solutions. The improved psychrometric chart is presented so that it not only displays temperature and humidity, but also displays a third weather parameter such as solar radiation, precipitation, wind speed, etc., with a specific time interval. This tool shows possibilities in effective visualization of the large-scale numeric data, utilization of raw data for the early stage architectural design, and development of re-sample methods for the big database.

Application of Numerical Weather Prediction Data to Estimate Infection Risk of Bacterial Grain Rot of Rice in Korea

Hyo-suk Kim,Ki Seok Do,Joo Hyeon Park,Wee Soo Kang,Yong Hwan Lee,Eun Woo Park 한국식물병리학회 2020 Plant Pathology Journal Vol.36 No.1

This study was conducted to evaluate usefulness of numerical weather prediction data generated by the Unified Model (UM) for plant disease forecast. Using the UM06- and UM18-predicted weather data, which were released at 0600 and 1800 Universal Time Coordinated (UTC), respectively, by the Korea Meteorological Administration (KMA), disease forecast on bacterial grain rot (BGR) of rice was examined as compared with the model output based on the automated weather stations (AWS)-observed weather data. We analyzed performance of BGRcast based on the UM-predicted and the AWS-observed daily minimum temperature and average relative humidity in 2014 and 2015 from 29 locations representing major rice growing areas in Korea using regression analysis and two-way contingency table analysis. Temporal changes in weather conduciveness at two locations in 2014 were also analyzed with regard to daily weather conduciveness (Ci) and the 20-day and 7-day moving averages of Ci for the inoculum build-up phase (Cinc) prior to the panicle emergence of rice plants and the infection phase (Cinf) during the heading stage of rice plants, respectively. Based on Cinc and Cinf , we were able to obtain the same disease warnings at all locations regardless of the sources of weather data. In conclusion, the numerical weather prediction data from KMA could be reliable to apply as input data for plant disease forecast models. Weather prediction data would facilitate applications of weather-driven disease models for better disease management. Crop growers would have better options for disease control including both protective and curative measures when weather prediction data are used for disease warning.

온실의 기간난방부하 산정을 위한 난방적산온도 비교분석

남상운,신현호,서동욱 (사) 한국생물환경조절학회 2014 생물환경조절학회지 Vol.23 No.3

To establish the design criteria for seasonal heating load calculation in greenhouses, standard weather dataare required. However, they are being provided only at seven regions in Korea. So, instead of using standard weatherdata, in order to find the method to build design weather data for seasonal heating load calculation, heating degreehourand heating degree-day were analyzed and compared by methods of fundamental equation, Mihara’s equationand modified Mihara’s equation using normal and thirty years from 1981 to 2010 hourly weather data provided byKMA and standard weather data provided by KSES. Average heating degree-hours calculated by fundamental equationusing thirty years hourly weather data showed a good agreement with them using standard weather data. The 24times 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 heatingload. And to conclude, in regions which are not available standard weather data, we suggest that design weather datashould be analyzed using thirty years hourly weather data. Average of heating degree-hours derived from every yearhourly weather data during the whole period can be established as environmental design standards, and also minimumand maximum of them can be used as reference data for energy estimation. 우리나라 각 지역별 온실의 기간난방부하 산정용 난방적산온도 자료를 구축하기 위해서는 표준기상데이터가필요하다. 그러나 국내에는 서울과 6대 광역시 등 7개지역만 표준기상데이터가 제공되고 있어서 이를 대체할수 있는 방법을 찾아야 한다. 전국적으로 이용이 가능한기상자료는 기상청의 일별 평년값 자료 및 30년(1981~2010)간 매 시각 전체 기상자료이므로 이를 이용하여 난방디그리아워와 난방디그리데이를 구하였다. 표준기상데이터가 있는 7개 지역을 대상으로 평년값 자료및 전체 기상자료를 사용하여 구한 난방디그리데이와 난방디그리아워를 표준기상데이터를 사용하여 구한 결과와비교하였다. 전체 기상자료를 이용하여 기본식으로 구한난방디그리아워의 평균값이 표준기상데이터로 구한 것과잘 일치하는 것으로 나타났다. 또한 평년값을 이용하여수정 Mihara식으로 구한 난방디그리아워도 표준기상데이터로 구한 것과 거의 비슷한 경향을 보였다. 이에 비하여 평년값을 이용하여 Mihara식으로 구한 난방디그리아워는 표준기상데이터로 구한 것 보다 훨씬 작았고 전체 기상자료의 최소값에 가까운 것으로 나타났다. 난방디그리아워와 동일한 단위로 환산했을 때, 난방 설정온도가 높을 경우에는 난방디그리데이와 난방디그리아워의차이가 별로 없었으나, 설정온도가 낮을 경우에는 난방디그리데이 방식이 난방디그리아워 방식보다 지역에 따라3~26%나 작게 나타나는 것으로 분석되었다. 난방디그리데이는 평년값을 이용하여 기본식으로 구할 수 있기 때문에간편한 방법이지만 설정온도가 낮을 경우 오차가 크게 발생되므로 난방디그리아워 방식이 더 합리적인 방법으로판단된다. 결론적으로 온실의 환경설계용 기상자료 구축에서 난방적산온도는 평년값과 동일한 30년간의 시간별 기상자료를 이용하여 매년 난방디그리아워를 구하고 전체자료기간의 평균값을 설계 자료로 활용할 것을 제안한다. 또한 최대 및 최소 난방디그리아워 자료를 제공함으로써기상상황에 따른 에너지 소비량 예측 및 경제성 평가에활용할 수 있도록 할 필요가 있을 것으로 판단된다.

스마트온실을 위한 가상 외부기상측정시스템 개발

한새론,이재수,홍영기,김국환,김성기,김상철 사단법인 인문사회과학기술융합학회 2015 예술인문사회융합멀티미디어논문지 Vol.5 No.5

This study was conducted to make use of Korea Meteorological Administration(KMA)’s Automatic Weather Station(AWS) data to operate smart green greenhouse. A Web-based KMA AWS data receiving system using JAVA and APM_SETUP 8 on windows 7 platform was developed. The system was composed of server and client. The server program was developed by a Java application to receive weather data from the KMA every 30 minutes and to send the weather data to smart greenhouse. The client program was developed by a Java applets to receive the KMA AWS data from the server every 30 minutes through communicating with the server so that smart greenhouse could recognize the KMA AWS data as the ambient weather information. This system was evaluated by comparing with local weather data measured by Inc. Ezfarm. In case of ambient air temperature, it showed some difference between virtual data and measured data. But, the average absolute deviation of the difference has a little difference as less than 2.24℃. Therefore, the virtual weather data of the developed system was considered available as the ambient weather information of the smart greenhouse. 오늘날 농촌의 인구가 고령화됨에 따라 농업 자동화는 필수가 되었다. 본 연구에서는 단동온실 자동화를 위한 외부 환경 측정 기술이 연구되었다. 연구를 위해 (주)이지팜에서 측정한 외부 환경 데이터를 사용하였다. 또한 Windows 7 환경에서 JAVA와 APM_SETUP 8을 이용하여 웹 기반의 기상청 AWS 데이터를 받는 시스템을 개발하였다. 스마트온실에 가상 외부기상데이터를 제공하기 위한 프로그램은 서버와 클라이언트로 구성되었다. 서버 프로그램은 30분마다 기상청으로부터 날씨 데이터를 받아서 스마트온실에 보내주도록 만들어졌다. 클라이언트 프로그램은 자바 애플릿으로 개발되어, 서버와 통신하여 30분마다 기상청 AWS 데이터를 받아서, 수신된 기상청 AWS 데이터를 스마트 온실 외부 환경 정보로 인식한다. 이 시스템은 (주)이지팜에서 측정한 기상 데이터와 비교함으로서 평가되었다. 외기 온도의 경우 기상청 AWS 데이터와 약간의 차이를 보였다. 그러나 평균절대편차는 2.24℃ 이하로 적은 차이를 보였다. 그러므로 개발된 가상 외부기상측정시스템의 날씨 정보는 스마트온실의 외부 날씨 정보로 사용될 수 있을 것이라 생각된다.

기상 및 환경 센서 데이터 기반 생육 환경 최적화 연구

전숙례 ( Sook Lye Jeon ),이진흥 ( Jinheung Lee ),김성억 ( Sung Eok Kim ),박정환 ( Jeonghwan Park ) 한국센서학회 2024 센서학회지 Vol.33 No.4

This study aimed to analyze the environmental factors affecting tomato growth by examining the correlation between weather and growth environment sensor data from P Smart Farm located in Gwangseok-myeon, Nonsan-si, Chungcheongnam-do. Key environmental variables such as the temperature, humidity, sunlight hours, solar radiation, and daily light integral (DLI) significantly affect tomato growth. The optimal temperature and DLI conditions play crucial roles in enhancing tomato growth and the photosynthetic efficiency. In this study, we developed a model to correct and predict the time-series variations in internal environmental sensor data using external weather sensor data. A linear regression analysis model was employed to estimate the external temperature variations and internal DLI values of P Smart Farm. Then, regression equations were derived based on these data. The analysis verified that the estimated variations in external temperature and internal DLI are explained effectively by the regression models. In this research, we analyzed and monitored smart-farm growth environment data based on weather sensor data. Thereby, we obtained an optimized model for the temperature and light conditions crucial for tomato growth. Additionally, the study emphasizes the importance of sensor-based data analysis in dynamically adjusting the tomato growth environment according to the variations in weather and growth conditions. The observations of this study indicate that analytical solutions using public weather data can provide data-driven operational experiences and productivity improvements for small- and medium-sized facility farms that cannot afford expensive sensors.