• Magazine of the Korean Society of Agricultural Engineers
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• v.61 no.4
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• pp.12-18
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• 2019

• New & Renewable Energy
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• v.20 no.1
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• pp.95-101
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• 2024

Since 1990, the Renewable Big Data Research Lab at the Korea Institute of Energy Technology has been observing solar radiation at 16 sites across South Korea. Serving as the National Reference Standard Data Center for Renewable Energy since 2012, it produces essential data for the sector. By 2020, it standardized meteorological year data from 22 sites. Despite user demand for data from approximately 260 sites, equivalent to South Korea's municipalities, this need exceeds the capability of measurement-based data. In response, our team developed a method to derive solar radiation data from satellite images, covering South Korea in 400,000 grids of 500 m × 500 m each. Utilizing satellite-derived data and ERA5-Land reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), we produced standard meteorological year data for 1,000 sites. Our research also focused on data measurement traceability and uncertainty estimation, ensuring the reliability of our model data and the traceability of existing measurement-based data.

• Journal of the Korean Solar Energy Society
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• v.38 no.6
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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.

• Journal of Bio-Environment Control
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• v.20 no.2
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• pp.83-92
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• 2011

This study is about an analysis of surplus solar energy by important greenhouse type using Typical Meteorological Year (TMY) data which was secured in order to provide basic data for designing an optimum thermal storage system to accumulate surplus solar energy generated in greenhouses during the daytime. The 07-auto-1 and 08-auto-1 types showed similar heat budget tendencies regardless of greenhouse types. In other words, the ratios of surplus solar energy were about 20.0~29.0% regardless of greenhouse type. About 54.0~225.0% and 53.0~218.0% of required heating energy will be able to be supplemented respectively according to the greenhouse types. The 07-mono-1 and 07-mono-3 types also showed similar heat budget tendencies regardless of greenhouse types. In other words, the ratios of surplus solar energy were about 20.0~26.0% and 21.0~27.0% respectively by greenhouse type. About 57.0~211.0% and 62.0~228.0% of required heating energy will be able to be supplemented by greenhouse type. Except for Daegwallyeong and Suwon area, other regions can cover heating energy only by surplus solar energy, according to the study.

• Journal of agriculture & life science
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• v.45 no.4
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• pp.135-141
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• 2011

This research was conducted to provide basic data of surplus heat for designing solar heat-storage systems. The surplus heat is defined as the heat exhausted by forced ventilations from the greenhouses to control the greenhouse temperature within setting limits. Various simulations were performed to compare the differences of thermal behaviors among greenhouse types as well as among several domestic areas by using pseudo-TMY (Typical Meteorological Year) data manipulated based both on the weather data supplied from Korean Meteorological Administration and the TMY data supplied from The Korean Solar Energy Society. Additional analyses were carried out to examine the required heating energy together with some others such as the energy balances in greenhouses to be considered. The results of those researches are summarized as follows. Regional surplus solar heats for the nine regions with 4-type were analyzed. The results showed that the ratio of surplus solar energy compared to heating energy was the highest in Jeju (about 212.0~228.0%) for each greenhouse type. And followed by Busan, Kwangju, Jinju, Daegu, Daejeon, Jeonju, Suwon and Daekwanryung. And irrespective of greenhouse types, surplus solar energy alone could cover up nearly all of the required supplemental heating energy except for a few areas.

• Journal of agriculture & life science
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• v.46 no.1
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• pp.195-206
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• 2012

This study is about an analysis of surplus solar energy by important greenhouse types as well as setting temperature different by using Typical Meteorological Year data which was secured in order to provide basic data for designing an optimum thermal storage system to accumulate surplus solar energy generating in greenhouses during the daytime. Depending on the setting temperatures of $15{\sim}19^{\circ}C$ for greenhouse heating during day and night, surplus heat amounts were varied at the rate of about $0.2{\sim}6.9%/4^{\circ}C$ with some variations according to the greenhouse types and regions. On the other hand, the variations of supplemental heat requirements were about $29.7{\sim}50.0%/4^{\circ}C$. Depending on the setting temperatures for greenhouse ventilations(low $25{\sim}29^{\circ}C$ and high $27{\sim}31^{\circ}C$), surplus heat amounts were varied at the rate of about $-9.9{\sim}-35.6%/4^{\circ}C$ in auto-type greenhouse. But in single-type greenhouses, they were about $-5.1{\sim}-13.4%/4^{\circ}C$. There were not significant changes in supplemental heat amounts depending on setting temperatures of ventilation for both greenhouse types and regions.

• New & Renewable Energy
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• v.20 no.1
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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.

• Journal of the Korean Solar Energy Society
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• v.39 no.3
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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.

• Journal of the Korean Solar Energy Society
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• v.39 no.3
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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.

• Journal of Bio-Environment Control
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• v.22 no.2
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• pp.91-99
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• 2013

This research analyzed surplus solar energy in Venlo-type greenhouse using acquired typical meteorological year (TMY) data for designing a heat storage system for the surplus solar energy generated in the greenhouse during the day. In the case of paprika, the region-dependent heating loads for Jeju, Jinju, and Daegwanryong area were approximately 1,107.8 GJ, 1,010.0 GJ, and 3,118.5 GJ, respectively. The surplus solar energy measured in Jeju area was 1,845.4 GJ, Jinju area 1,881.8 GJ, and Daegwanryong area 2,061.8 GJ, with the Daegwanryong area showing 11.7% and 9.6% higher than the Jeju region and Jinju region respectively. In the case of chrysanthemums, regional heating loads were determined as 1,202.5 GJ for the Jeju region, 1,042.0 GJ for the Jinju region, and 3,288.6 GJ for the Daegwanryong region; the regional differences were similar to those for paprika. The recorded surplus solar energy was 1,435.2 GJ, 1,536.2 GJ, and 1,734.6 GJ for Jeju, Jinju, and Daegwanryong region, respectively. The Daegwanryong region recorded heating loads 20.9% and 12.9% higher than in the Jeju and Jinju region, respectively. From the above, it can be said that cultivating paprika, compared to cultivating chrysanthemums, requires less heating energy regardless of the region and tends to yield more surplus solar energy. Moreover, if the Daekwan Pass region is excluded, the surplus solar energy exceeds the energy required for heating. Although the required heating energy differs according to regions and crops, cucumbers were found to require the highest amount, followed by chrysanthemum and paprika. The amount of surplus solar energy was the highest in the case of paprika, followed by cucumber and chrysanthemum.