• 응용통계연구
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• 제22권5호
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• pp.943-960
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• 2009

본 연구는 한반도 바람자원 TMY(typical meteorological year)의 구축에 적절한 알고리즘을 제안하고, 이를 전국 77개 기상관측소에서 1998년~2008년 기간 동안 관측한 바람자료에 적용하여 TMY를 구축하였다. 제안된 알고리즘은 Filkenstein-Shafer(FS) 통계모형 하에서 정의된 다양한 통계를 사용하여 연/원별 바람자료의 설명력 측도인 TMM(typical meteorological month)점수를 구하고, TMM점수에 기준하여 TMY를 구축하는 절차이다. 알고리즘은 두 단계 계산알고리즘으로 구성되었으며, 첫 단계는 각 관측소의 바람개황 그리고 둘째 단계는 한반도의 바람개황을 대표하는 TMY가 되도록 설계하였다. 11년 바람자료와의 비교분석, 경쟁모형에 의해 구축된 TRY(typical reference year)들과의 비교, 기상요소 추가에 따른 TMY의 영향평가 등 여러 종류의 비교 및 평가를 통하여 한반도 바람자원의 개황에 대한 TMY의 대표성과 효용성을 보였다.

• 신재생에너지
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• 제8권2호
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• pp.14-23
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• 2012

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.

• 한국태양에너지학회 논문집
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• 제39권3호
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• pp.67-79
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• 2019

The reliable Typical Meteorological Year (TMY) data, sometimes called Test Reference Year (TRY) data, are necessary in the feasibility study of renewable energy installation as well as zero energy building. In Korea, there are available TMY data; TMY from Korea Institute of Energy Research (KIER), TRY from the Korean Solar Energy Society (KSES) and TRY from Passive House Institute Korea (PHIKO). This study aims at examining their characteristics by using Principle Component Analysis (PCA) at six ground observing stations. First step is to investigate the annual averages of meteorological elements from TMY data and their standard deviations. Then, PCA is done to find which principle components are derived from different TMY data. Temperature and solar irradiance are determined as the main principle component of TMY data produced by KIER and KSES at all stations whereas TRY data from PHIKO does not show similar result from those by KIER and KSES.

• 신재생에너지
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• 제20권1호
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• pp.102-109
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• 2024

The Typical Meteorological Year (TMY) dataset compiles 12 months of data that best represent long-term climate patterns, focusing on global horizontal irradiance and other weather-related variables. However, the irradiance measured on the plane of the array (POA) shows certain distinct distribution characteristics compared with the irradiance in the TMY dataset, and this may introduce some biases. Our research recalculated POA irradiance using both the Isotropic and DIRINT models, generating an updated dataset that was tailored to POA characteristics. Our analysis showed a 28% change in the selection of typical meteorological months, an 8% increase in average irradiance, and a 40% reduction in the range of irradiance values, thus indicating a significant shift in irradiance distribution patterns. This research aims to inform stakeholders about accurate use of TMY datasets in potential decision-making. These findings underscore the necessity of creating a typical dataset by using the time series of POA irradiance, which represents the orientation in which PV panels will be deployed.

• 한국태양에너지학회 논문집
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• 제38권6호
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• pp.27-36
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• 2018

Typical Meteorological Year Dataset is necessary for the renewable energy feasibility study. Since National Renewable Energy Laboratory has been built Typical Meteorological Year Dataset in 1978, gridded datasets taken from numerical weather prediction or satellite imagery are employed to produce Typical Meteorological Year Dataset. In general, Typical Meteorological Year Dataset is generated by using long-term in-situ observations. However, solar insolation is not usually measured at synoptic observing stations and therefore it is limited to build the Typical Meteorological Year Dataset with only in-situ observation. This study attempts to build the Typical Meteorological Year Dataset with satellite derived solar insolation as an alternative and then we evaluate the Typical Meteorological Year Dataset made by using satellite derived solar irradiance at Daejeon ground station. The solar irradiance is underestimated when satellite imagery is employed.

• 한국태양에너지학회 논문집
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• 제39권3호
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• pp.81-89
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• 2019

Long-term solar irradiance data are required for reliable performance evaluation and feasibility analysis of solar photovoltaic systems. However, measurement data of the global horizontal irradiance (GHI) are only available for major cities in Korea. Neither the direct normal irradiance (DNI) nor the diffuse horizontal irradiance (DHI) are available, which are also needed to calculate the irradiance on the tilted surface of PV array. It is a simple approach to take advantage of the decomposition model that extracts DNI and DHI from GHI. In this study, we investigate variations of solar PV power estimation due to the choice of decomposition model. The GHI data from Korea Meteorological Administration (KMA) were used and different sets of typical meteorological year (TMY) data using some well-known decomposition models were generated. Then, power outputs with the different TMY data were calculated, and a variation of 3.7% was estimated due to the choice of decomposition model.

• 응용통계연구
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• 제23권6호
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• pp.1157-1167
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• 2010

본 논문은 남한지역 풍력자원의 계량화 및 바람환경분석 등에 필요한 풍력에너지지도를 고해상도로 작성하는 방법을 제안하였다. 이를 위해 $1Km{\times}1Km$ 격자로 나누어진 남한전역(345,682 지점)의 월별풍속에 적합한 통계적 바람장모형을 설정하여 각종 풍력에너지통계를 $1Km{\times}1Km$ 격자지점 별로 계산하고, 통계값들를 지도로 구현하는 절차를 연구하였다. 바람장모형의 적합성검정에는 국내 76개 기상관측소에서 관측된 TMY (typical meteorological year) 바람자료가 사용되었으며, Kolmogrov-Smirnov 검정결과 로그정규모형이 남한지역의 월별 바람장모형에 적합하였다. 또한 로그정규모형 하에서 얻어지는 다양한 형태의 풍력에너지통계들을 소개하였으며, 국립기상연구소가 제공하는 $1Km{\times}1Km$ 격자지점(345,682 지점)의 풍속자료를 사용하여 남한(지상 80m)의 풍력에너지밀도(W/$m^2$)지도를 공간분포도 형태로 작성해 보였다.

• 한국태양에너지학회 논문집
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• 제32권2호
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• pp.64-73
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• 2012

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 it save rage time is12:23. The maximum solar radiation is 1012$W/m^2$ and 708$W/m^2$ inc lear 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. Correlationis 0.95 and RMSE (Root Mean Square Error) is10.81 MJ. The result shows that optimum N-S slope angles of solar panel are about $2^{\circ}$ lower than station latitude, but E-W slope angles are lower than ${\pm}1^{\circ}$. 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.

• 융복합기술연구소 논문집
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• 제7권2호
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• pp.7-12
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• 2017

At present, the Korea Meteorological Administration (KMA) measures the horizontal solar irradiation and meteorological data with time in 33 areas. Among these measured data, this study analyzed the tendency of applying the new analysis method by using the horizontal solar irradiation and meteorological data with the time which was measured in many regions across the country for thirty years from 1985 to 2014. The method applied to the analysis is to compare the value of the annual total horizontal solar irradiance and meteorological data for one year with the value of those for the previous year of each year, and give +1 when it is higher, and -1 when it is lower. The characteristics and relationships the horizontal solar irradiation and meteorological data in Daegu were evaluated and analyzed. Through the analysis results, the analysis method applied in this study could be well describe the characteristics and relationships of the solar irradiance and meteorological data during some years.

• 생물환경조절학회지
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• 제20권2호
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• pp.83-92
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• 2011

본 연구는 주간에 온실 내에서 환기로 인하여 배출되는 잉여 태양에너지를 축열할 적정 축열 시스템 설계의 기초자료를 제공할 목적으로 확보한 표준기상년(TMY; Typical Meteorological Year) 데이터를 이용하여 주요 온실 형태별로 잉여 태양에너지를 분석하였다. 그 연구결과를 요약하면 다음과 같다. 07-자동화-1형 및 08-자동화-1형의 경우, 온실형태에 관계없이 매우 유사한 열수지 경향을 보였다. 즉, 잉여 태양에너지가 차지하는 비율은 온실 형태별로 각각 약 20.0~29.0% 및 20.0~29.0% 정도로 나타났다. 그리고 소요 난방에너지를 온실 형태별로 각각 약 54.0~225.0% 및 53.0~218.0% 정도 보충할 수 있을 것으로 나타났다. 07-단동-1형과 07-단동-3형의 경우도 온실형태에 관계없이 매우 유사한 열수지 경향을 보였다. 즉, 잉여태양에너지가 차지하는 비율은 온실 형태별로 각각 약 20.0~26.0% 및 21.0~27.0% 정도로 나타났다. 그리고 소요 난방에너지를 온실 형태별로 각각 약 57.0~211.0% 및 62.0~228.0% 정도 보충할 수 있는 량이다. 그리고 온실형태에 관계없이 대관령 및 수원지역을 제외하면 나머지 지역은 잉여 태양에너지만으로도 난방에너지를 충당할 수 있음을 알 수 있었다.