한반도 태양에너지 연구를 위한 일사량 자료의 TMY 구축

지준범(Jee, Joon-Bum),이승우(Lee, Seung-Woo),최영진(Choi, Young-Jean),이규태(Lee, Kyu-Tae) 한국신재생에너지학회 2012 신재생에너지 Vol.8 No.2

The TMY (Typical Meteorological Year) for the solar energy study is generated using observation data with 22 solar sites from KMA (Korea Meteorological Administration) during 11 years (2000-2010). The meteorological data for calculation the TMY are used solar radiation, temperature, dew point temperature, wind speed and humidity data. And the TMY is calculated to apply the FS (Finkelstein and Schafer) statistics and RMSE (Root Mean Squared Error) methods. FS statistics performed with each point and each variable and then selected top five candidate TMM months with statistical analysis and normalization. Finally TMY is generated to select the highest TMM score with evaluation the average errors for the 22 whole points. The TMY data is represented average state and long time variations with 22 sites and meteorological data. When TMY validated with the 11-year daily solar radiation data, the correlation coefficient was about 0.40 and the highest value is 0.57 in April and the lowest value is 0.23 in May. Mean monthly solar radiation of TMY is 411.72 MJ which is 4 MJ higher than original data. Average correlation coefficient is 0.71, the lowest correlation is 0.43 in May and the highest correlation is 0.90 in January. Accumulated annual solar radiation by TMY have higher value in south coast and southwestern region and have relatively low in middle regions. And also, differences between TMY and 11-year mean of is distributed lower 100 MJ in Kyeongbuk, higher 200 MJ in Jeju and higher 125 MJ in Jeonbuk and Jeonnam, respectively.

Development of Solar-Meteorological Resources Map using One-layer Solar Radiation Model Based on Satellites Data on Korean Peninsula

지준범(Jee, Joonbum),최영진(Choi, Youngjean),이규태(Lee, Kyutae),조일성(Zo, Ilsung) 한국신재생에너지학회 2011 한국신재생에너지학회 학술대회논문집 Vol.2011 No.11

The solar and meteorological resources map is calculated using by one-layer solar radiation model (GWNU model), satellites data and numerical model output on the Korean peninsula. The Meteorological input data to perform the GWNU model are retrieved aerosol optical thickness from MODIS (TERA/AQUA), total ozone amount from OMI (AURA), cloud fraction from geostationary satellites (MTSAT-1R) and temperature, pressure and total precipitable water from output of RDAPS (Regional Data Assimilation and Prediction System) and KLAPS (Korea Local Analysis and Prediction System) model operated by KMA (Korea Meteorological Administration). The model is carried out every hour using by the meteorological data (total ozone amount, aerosol optical thickness, temperature, pressure and cloud amount) and the basic data (surface albedo and DEM). And the result is analyzed the distribution in time and space and validated with 22 meteorological solar observations. The solar resources map is used to the solar energy-related industries and assessment of the potential resources for solar plant. The National Institute of Meteorological Research in KMA released 4km{times}4km solar map in 2008 and updated solar map with 1km{times}1km resolution and topological effect in 2010. The meteorological resources map homepage (http://www.greenmap.go.kr) is provided the various information and result for the meteorological-solar resources map.

집중관측 자료를 이용한 춘천기상대 태양광 패널의 온도 및 태양광 발전량 분석

지준범(Jee Joon-Bum),조일성(Zo Il-Sung),이규태(Lee Kyu-Tae),이원학(Lee Won-Hak),최성진(Choi Sung-Jin) 한국태양에너지학회 2022 한국태양에너지학회 논문집 Vol.42 No.2

In this study, photovoltaic (PV) electricity power and PV panel temperature for operation and monitoring of PV power plant were calculated and analyzed. A PV panel temperature sensor was installed at the Chuncheon Meteorological Observatory solar power plant for intensive observation from May 1 to August 19, 2018. When the calculated PV panel temperature was analyzed using the measured PV panel temperature, the calculated PV panel temperature was overestimated at a higher temperature compared to the measured PV panel temperature, which was overestimated at a lower temperature; however, the determination coefficient (R²) was 0.88 or more. The bias was -0.33°C and RMSE was 3.43°C when the ground observation data were used. However, when the Local Data Assimilation and Prediction (LDAPS) model were used, the bias was 0.22°C and RMSE was 4.27°C. The PV electricity power generation by ground meteorological observation data (Korea Meteorological Administration, KMA), LDAPS model prediction data (LDAPS), and Communication Ocean and Meteorological Satellite (COMS) data using the PV module temperature were compared with those of the Chuncheon PV power plant. The determination coefficient (R²) of PV power generation was the highest for KMA (0.91) followed by COMS (0.88) and LDAPS (0.84). The slope of the linear regression, (1.05) for KMA, and the smallest bias (2.24 kWh) and RMSE (3.38 kWh) were similar to the measured values. However, compared to the LDAPS, the slope (1.23) of the linear regression was the largest in COMS, and the bias (4.77 kWh) and RMSE (6.23 kWh) were slightly higher.

강원도 지자체별 관광객 수 예측 알고리즘 개발

지준범(Jee, Joon-Bum),조일성(Zo, Il-Sung),배준호(Bae, Joon-Ho) 한국관광레저학회 2022 관광레저연구 Vol.34 No.10

In this study, a machine learning-based model was built to predict the number of visitors with municipality in Gangwon province using big data with tourist, meteorological observation (air temperature, rainfall, wind speed, wind direction, relative humidity, atmospheric pressure, sunshine duration, solar radiation and cloud fraction) and temporal variables (day, week, and year). The relative influence of meteorological variables was found to be 37.9% on average in Gangwon province through the contribution analysis by input data. As a result of annual predictive analysis, the correlation is 0.81 on average in Gangwon province, with the highest municipality is Inje-gun (0.86) and the lowest municipality is Cheorwon-gun (0.73). And as a result of seasonal analysis, summer (0.93) represents the highest correlation, followed by winter (0.76), spring (0.74), and autumn (0.66). The municipality with the lowest and highest RMSE compared to the average daily number of visitors are Wonju-si (16.6%) and Yeongwol-gun (33.1%), respectively.

다층 상세 태양복사 모델에 의한 단층 태양복사 모델의 보정

지준범(Joon-Bum Jee),이원학(Won-Hak Lee),조일성(Il-Sung Zo),이규태(Kyu-Tae Lee) 한국기상학회 2011 대기 Vol.21 No.2

권운 내 빙정의 종류와 크기에 따른 복사 평형 온도 변화

지준범(Joon-bum Jee),이원학(Won-Hak Lee),이규태(Kyu-Tae Lee) 한국기상학회 2010 대기 Vol.20 No.4

1km 해상도 태양-기상자원지도 기반의 초고해상도 태양 에너지 분석

지준범(Jee, JoonBum),조일성(Zo, Ilsung),이채연(Lee, Chaeyon),최영진(Choi, Youngjean),김규랑(Kim, Kyurang),이규태(Lee, KyuTae) 한국신재생에너지학회 2013 신재생에너지 Vol.9 No.2

The solar energy are an infinite source of energy and a clean energy without secondary pollution. The global solar energy reaching the earth's surface can be calculated easily according to the change of latitude, altitude, and sloped surface depending on the amount of the actual state of the atmosphere and clouds. The high-resolution solar-meteorological resource map with 1km resolution was developed in 2011 based on GWNU (Gangneung-Wonju National University) solar radiation model with complex terrain. The very high resolution solar energy map can be calculated and analyzed in Seoul and Eunpyung with topological effect using by 1km solar-meteorological resources map, respectively. Seoul DEM (Digital Elevation Model) have 10m resolution from NGII (National Geographic Information Institute) and Eunpyeong new town DSM (Digital Surface Model) have 1m spatial resolution from lidar observations. The solar energy have small differences according to the local mountainous terrain and residential area. The maximum bias have up to 20% and 16% in Seoul and Eunpyung new town, respectively. Small differences are that limited area with resolutions. As a result, the solar energy can calculate precisely using solar radiation model with topological effect by digital elevation data and its results can be used as the basis data for the photovoltaic and solar thermal generation.

서울지역의 고해상도 WISE-WRF 모델의 지표면 거칠기 길이 개선에 따른 민감도 분석

지준범(Joon-Bum Jee),장민(Min Jang),이채연(Chaeyeon Yi),조일성(Il-Sung Zo),김부요(Bu-Yo Kim),박문수(Moon-Soo Park),최영진(Young-Jean Choi) 한국기상학회 2016 대기 Vol.26 No.1

고해상도 Landsat 8 위성자료기반의 지표면 온도 산출

지준범 ( Joon Bum Jee ),김부요 ( Bu Yo Kim ),조일성 ( Il Sung Zo ),이규태 ( Kyu Tae Lee ),최영진 ( Young Jean Choi ) 대한원격탐사학회 2016 大韓遠隔探査學會誌 Vol.32 No.2

2013년부터 2014년까지 관측된 Landsat 8 위성자료로부터 지표면 온도를 산출하였고 산출된 지표면 온도는 지상에서 관측된 지표면 온도를 이용하여 보정하였다. 지표면 온도지도는 Landsat 8로부터 산출된 지표면 온도를 지상에서 관측된 지표면 온도와의 선형 회귀식을 이용하여 계산되었다. 계절과 년에 대한 지표면 온도는 각각 계절과 년에 대하여 사례들을 평균하여 계산되었다. 지표면 온도는 도시의 공업 또는 상업지역에서 높은 온도가 나타나는 반면, 서울주변의 높은 고도의 산악과 해양, 강 등에서 낮은 지표면 온도가 나타났다. 위성에서 산출된 지표면 온도를 보정하기 위하여 서울을 포함한 수도권지역에서 관측되는 기상청 종관측소 3개 지점 (서울(지점번호: 108), 인천(지점번호: 119), 수원(지점번호: 112))의 지표면 관측자료를 이용하여 선형회귀방법을 적용하였다. Landsat 8의 지표면 온도는 모든 자료에서 기울기가 0.78이었고 5개의 흐린날을 제외한 맑은 상태의 자료에서 0.88이었다. 그리고 초기 지표면온도에서 상관계수는 0.88이었고 평방근 오차 (Root Mean Sqare Error (RMSE))는 5.33℃이었다. 지표면 온도 보정이후에는 상관계수는 0.98 그리고 RMSE는 2.34℃이었으며 회귀식의 기울기는 0.95로 개선되었다. 계절 및 년 지표면 온도는 상업지역과 공업지역 그리고 도시와 주변지역을 잘 표현하였다. 결과적으로 지상에서 관측된 지표면 온도를 이용하여 위성에서 산출된 지표면온도를 보정하였을 때 실제 상태와 유사한 분포를 보였다. Land Surface Temperature (LST) retrieved from Landsat 8 measured from 2013 to 2014 and it is corrected by surface temperature observed from ground. LST maps are retrieved from Landsat 8 calculate using the linear regression function between raw Landsat 8 LST and ground surface temperature. Seasonal and annual LST maps developed an average LST from season to annual, respectively. While the higher LSTs distribute on the industrial and commercial area in urban, lower LSTs locate in surrounding rural, sea, river and high altitude mountain area over Seoul and surrounding area. In order to correct the LST, linear regression function calculate between Landsat 8 LST and ground surface temperature observed 3 Korea Meteorological Administration (KMA) synoptic stations (Seoul(ID: 108), Incheon(ID: 112) and Suwon(ID: 119)) on the Seoul and surrounding area. The slopes of regression function are 0.78 with all data and 0.88 with clear sky except 5 cloudy pixel data. And the original Landsat 8 LST have a correlation coefficient with 0.88 and Root Mean Square Error (RMSE) with 5.33°C. After LST correction, the LST have correlation coefficient with 0.98 and RMSE with 2.34°C and the slope of regression equation improve the 0.95. Seasonal and annual LST maps represent from urban to rural area and from commercial to industrial region clearly. As a result, the Landsat 8 LST is more similar to the real state when corrected by surface temperature observed ground.

태양복사모델을 이용한 태양전지판의 최적 경사각에 대한 연구

지준범(Jee Joon-Bum),최영진(Choi Young-Jean),이규태(Lee Kyu-Tae) 한국태양에너지학회 2012 한국태양에너지학회 논문집 Vol.32 No.2

The angle of solar panels is calculated using solar radiation model for the efficient solar power generation. In ideal state, the time of maximum solar radiation is represented from 12:08 to 12:40 during a year at Gangneung and its average time is 12:23. The maximum solar radiation is 1012 W/㎡ and 708 W/㎡ in clear sky and cloudy sky, respectively. Solar radiation is more sensitive to North-South (N-S) slope angle than East-West (E-W) azimuth angle. Daily solar radiation on optimum angle of solar panel is higher than that on horizontal surface except for 90 days during summer. In order to apply to the real atmosphere, the TMY (typical meteorological Year) data which obtained from the 22 solar sites operated by KMA(Korea Meteorological Administration) during 11 years(2000 to 2010) is used as the input data of solar radiation model. The distribution of calculated solar radiation is similar to the observation, except in Andong, where it is overestimated, and in Mokpo and Heuksando, where it is underestimated. Statistical analysis is performed on calculated and observed monthly solar radiation on horizontal surface, and the calculation is overestimated from the observation. Correlation is 0.95 and RMSE (Root Mean Square Error) is 10.81 MJ. The result shows that optimum N-S slope angles of solar panel are about 2º lower than station latitude, but E-W slope angles are lower than ± 1º. There are three types of solar panels: horizontal, fixed with optimum slope angle, and panels with tracker system. The energy efficiencies are on average 20% higher on fixed solar panel and 60% higher on tracker solar panel than compared to the horizontal solar panel, respectively.