Boyoung Kim

Accurate solar irradiance data are essential for photovoltaic (PV) resource assessment in Indonesia, yet no hourly-resolution multi-database validation exists across the archipelago scale. This study evaluates four gridded solar irradiance databases, CAMS, JAXA Himawari-9, NASA POWER, and ERA5, against quality-controlled ground measurements from 26 BMKG stations spanning all three major Köppen climate zones (Af, Am, Aw) during 2023–2024. CAMS achieved the highest hourly correspondence (median R² = 0.855, rRMSE = 28.1%), and JAXA was close behind (R² = 0.848), while NASA POWER showed the lowest systematic bias (rMBE = +3.6%). ERA5 showed the largest hourly errors (R² = 0.617, rRMSE = 41.2%) consistent with its model-generated cloud fields that are unable to reproduce the localized convective-cloud variability. Because CAMS and JAXA infer cloud from the same Himawari-9 AHI observations on a common 0.05° grid, their comparison reduced sensor and resolution-related differences and focused on the retrieval design. Both showed closely comparable performance (clear-sky R² = 0.925 vs. 0.913), and this gap widens as cloud frequency increases across the three climate zones. All four databases showed substantially larger relative errors under overcast conditions (Kt < 0.35); the temporal aggregation reduced relative error substantially, with CAMS showing the lowest error at hourly and daily scales and NASA POWER the lowest monthly rRMSE. These results provide an archipelago-scale hourly-resolution validation of four structurally distinct databases across Indonesia’s three tropical climate zones, supporting product selection and bias-correction strategies for solar resource application in Indonesia.

This study presents a machine-learning framework for predicting spectral solar irradiance from 300 to 1100 nm using the random forest and neural network models. Two approaches were compared: one using the real aerosol optical depth at discrete wavelengths and the other using the synthetic aerosol optical depth derived from the Ångström exponent. The models were trained on atmospheric variables and were validated against high-resolution spectroradiometer data from the Korea Institute of Energy Research. The models based on the synthetic aerosol optical depth outperformed real-data models in terms of accuracy, bias, and variance explained (γ² = 0.979 vs. 0.967). These models also provide more reliable estimates of the spectral mismatch factor across the diverse photovoltaic technologies. The copper indium gallium diselenide and mono-crystalline silicon cells showed a high spectral mismatch factor stability, whereas Perovskite cells exhibited a greater sensitivity to spectral variations.

Satellite imagery-based solar irradiance mapping studies are essential for large-scale solar energy assessments but are limited in spatial resolution and accuracy. Despite efforts to increase map resolution by correcting inaccuracies caused by shadows on the terrain, the computational time of these models and the massive volume of generated data still pose challenges. Particularly, forecasting generates large amounts of time series data, and the data production rate is faster than the computational speed of traditional terrain correction. Moreover, while previous research has been conducted to expedite computations, a novel and innovative technology in terrain correction is still required. Therefore, we propose a new correction method that can bypass complex calculations and process enormous data within seconds. This model extends the lookup table concept, optimizes the results of many shadow operations, and stores them in memory for use. The model enabled 90 m scale computations across Korea within seconds on a local desktop computer. Optimization was performed based on domain knowledge to reduce the required memory to a realistic level. A quantitative analysis of computation time was also conducted, revealing a previously overlooked computational bottleneck. In conclusion, the developed model enables real-time terrain correction and subsequent processing of massive amounts of data.

Photovoltaic potential is directly affected by solar radiation. In particular, in the case of densely populated areas, the shadow effect has a fatal effect on the reduction of insolation on the building surface. In this study, in order to calculate the building-type photovoltaic market potential in Korea, the solar radiation reduction statistics due to shadow was extracted by region and applied to the calculation. We created building models with 1m spatial resolution and extracted solar radiation data for individual points of each building surface using Rhinoceros software and plugins Grasshopper and Ladybug tools. To verify the performance of the applied model, the results were compared with the other simulation model output. In order to extract the standard coefficient to be applied to the whole country, the sensitivity according to the data distribution such as building density, building orientation, building height in the target area was analyzed. The market potential is affected by the solar power supply curve in connection with LCOE(Levelized Cost of Electricity) and site condition for the energy facility. For the simulation, the entire Korean peninsula was divided into 1km × 1km grids and exclusion area values were input into the each grid through GIS(Geographical Information System) calculation. The market potential is affected by the SMP (System Marginal Price) and REC(Renewable Energy Certificate) conditions at the base time point. If this calculation model is used, it is possible to simulate the change in market potential due to changes in regulatory and support policies. The results were created in the form of a distribution map so that quantitative figures by region can be revealed. Using the supply curve derived in this study, the market potential that can actually be supplied under the current market conditions was analyzed.

Renewable energy supply is essential for carbon neutrality; however, technologies aiming to optimally utilize renewable energy sources remain insufficient. Seasonal variability in renewable energy is a key issue, which many studies have attempted to overcome through operating systems and energy storage. Currently, hydrogen is the only technology that can solve this seasonal storage problem. In this study, the amount of hydrogen required to circumvent the seasonal variability in renewable energy supply in Korea was quantified. Spatiotemporal analysis was conducted using renewable energy resource maps and power loads. It was predicted that 50% of the total power demand in the future will be met using solar and wind power, and a scenario was established based on the solar-to-wind ratio. It was found that the required hydrogen production differed by approximately four-times, depending on the scenarios, highlighting the importance of supplying renewable energy at an appropriate ratio. Spatially, wind power was observed to be unsuitable for the physical transport of hydrogen because it has a high potential at mountain peaks and islands. The results of this study are expected to aid future hydrogen research and solve renewable energy variability problems.

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.