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The relative mean bias error for the monthly mean of daily total irradiance that is estimated by the UASBIS-KIER model with satellite imagery from 1996 to 2019.
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The Korea Institute of Energy Research builds Korean solar irradiance datasets, using gridded solar insolation estimates derived using the University of Arizona solar irradiance based on Satellite-Korea Institute of Energy Research (UASIBS-KIER) model, with the incorporation of geostationary satellites over the Korean Peninsula, from 1996 to 2019....
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... The nominal root mean square error (nRMSE) was 6.73% on average over the Korean peninsula, and the lowest nRMSE was in the southeast (SE) region. This may be because the SE region receives a higher annual solar irradiance than the rest of South Korea [19]. Finally, the locations of each substation were mapped onto all grid cells over the Korean peninsula. ...
To achieve the goal of carbon neutrality, increasing the contribution of renewable energy sources (RESs) such as solar and wind to power grids is necessary. However, existing energy management systems are not well-equipped to handle the inherent volatility of RESs, and previous attempts to develop new management systems have mostly been limited to small scales such as microgrids and buildings. The Korea Electrotechnology Research Institute and Korea Institute of Energy Research jointly developed a renewables management system (RMS) for large-scale grids that comprises four parts: a 12-h-ahead solar irradiance forecast model; look-ahead horizon stability assessment; generation o f future RES penetration scenarios by a generative adversarial networks model; and confidence level-based adaptive droop control strategy for energy storage systems. In particular, the adaptive droop control obtains the joint probability distribution at each substation based on copula theory, and the droop gain changes with the confidence level. Simulations were performed to demonstrate the effectiveness of the proposed RMS at managing large-scale grids with high RES penetration.
... The UASIBS-KIER model has been implemented with various satellite imagery, resulting in different time-series product time resolutions [28]. Despite the long temporal coverage of solar irradiance time series (up to 23 years of historical data) [6], the requirements of site-adaptation processes for coincidence quality-controlled ground measurement data limit the time period that can be used. In this study, we used data from 2014 to 2019 where the UASIBS-KIER model used the COMS Level-1B data observed by the Meteorological Imager onboard the COMS satellite over the Korean Peninsula with 15-min temporal resolution. ...
Satellite-derived solar irradiance is advantageous in solar resource assessment due to its high spatiotemporal availability, but its discrepancies to ground-observed values remain an issue for reliability. Site adaptation can be employed to correct these errors by using short-term high-quality ground-observed values. Recent studies have highlighted the benefits of the sequential procedure of a regressive and a distribution-mapping technique in comparison to their individual counterparts. In this paper, we attempted to improve the sequential procedure by using various distribution mapping techniques in addition to the previously proposed quantile mapping. We applied these site-adaptation techniques on the global horizontal irradiance (GHI) and direct normal irradiance (DNI) obtained from the UASIBS-KIER model in Daejeon, South Korea. The best technique, determined by a ranking methodology, can reduce the mean bias from −5.04% and 13.51% to −0.45% and −2.02% for GHI and DNI, respectively, and improve distribution similarity by 2.5 times and 4 times for GHI and DNI, respectively. Partial regression and residual plot analysis were attempted to examine our finding that the sequential procedure is better than individual techniques for GHI, whereas the opposite is true for DNI. This is an initial study to achieve generalized site-adaptation techniques for the UASIBS-KIER model output.
... At present, only 17 of the Chinese 119 ground-based observation stations can provide diffuse solar radiation observations, and these stations have different starting times, thus making it difficult to capture the distribution of diffuse solar radiation in detail. In contrast, satellite remote sensing provides a unique perspective [9] and has been demonstrated to be a reliable method for monitoring geographical and temporal fluctuations in diffuse solar radiation at global and regional scales [10]. The Heliosat model [11,12], cloud index methods [13], Meteonom database [14], and EU-PVGIS [14] are now the most commonly used approaches for retrieving diffuse solar radiation from satellite remote sensing. ...
Diffuse solar radiation is an essential component of surface solar radiation that contributes to carbon sequestration, photovoltaic power generation, and renewable energy production in terrestrial ecosystems. We constructed a 39-year (1982–2020) daily diffuse solar radiation dataset (CHSSDR), using ERA5 and MERRA_2 reanalysis data, with a spatial resolution of 10 km through a developed ensemble model (generalized additive models, GAM). The validation results, with ground-based measurements, showed that GAM had a high and stable performance with the correlation coefficient (R), root-mean-square error (RMSE), and mean absolute error (MAE) for the sample-based cross-validations of 0.88, 19.54 Wm−2, and 14.87 Wm−2, respectively. CHSSDR had the highest consistency with ground-based measurements among the four diffuse solar radiation products (CERES, ERA5, JiEA, and CHSSDR), with the least deviation (MAE = 15.06 Wm−2 and RMSE = 20.22 Wm−2) and highest R value (0.87). The diffuse solar radiation values in China range from 59.13 to 104.65 Wm−2, with a multi-year average value of 79.39 Wm−2 from 1982 to 2020. Generally, low latitude and low altitude regions have larger diffuse solar radiation than high latitude and high altitude regions, and eastern China has less diffuse solar radiation than western China. This dataset would be valuable for analyzing regional climate change, photovoltaic applications, and solar energy resources. The dataset is freely available from figshare.
A study was conducted to analyze data from 1981 to 2020 for understanding the impact of climate on solar energy generation. A significant increase of 104.6 kWhm-2 was observed in the annual cumulative solar radiation over this period. Notably,the distribution of solar radiation shifted, with the solar radiation in Busan rising from the seventh place in 1981 to the second place in 2020 in South Korea. This study also examined the correlation between long-term temperature trends and solar radiation. Areas with the highest solar radiation in 2020, such as Busan, Gwangju, Daegu, and Jinju, exhibited strong positive correlations, suggesting that increased solar radiation contributed to higher temperatures. Conversely, regions like Seosan and Mokpo showed lower temperature increases due to factors such as reduced cloud cover. To evaluate the impact on solar energy production, simulations were conducted using climate data from both years. The results revealed that relying solely on historical data for solar energy predictions could lead to overestimations in some areas, including Seosan or Jinju, and underestimations in others such as Busan. Hence, considering long-term climate variability is vital for accurate solar energy forecasting and ensuring the economic feasibility of solar projects.
