Hyun-Goo Kim

Aerosol optical depth characterizes total aerosol loading in the atmospheric column but does not directly indicate its vertical distribution relative to the planetary boundary layer. This study investigates aerosol vertical structure over Daejeon, Korea, by integrating column aerosol optical properties from a sky radiometer, near-surface atmospheric extinction measurements, and planetary boundary layer height from reanalysis data. Aerosol scale height is derived from aerosol optical depth at 870 nm and near-surface extinction and is analyzed alongside aerosol optical and microphysical properties. Aerosol scale height exhibits substantial variability relative to the planetary boundary layer height, with the highest relative values occurring in spring and the lowest in autumn. An increase in aerosol scale height relative to the boundary layer is accompanied by a decrease in the Ångström exponent and an increase in the coarse-mode volume fraction. The coarse-mode enhancement is driven mainly by an increase in coarse-mode aerosol volume, whereas surface PM₁₀ concentrations show only a weak relationship with variations in vertical structure. Single scattering albedo generally increases, indicating a more scattering dominated column aerosol population. These results demonstrate that combining aerosol scale height with planetary boundary layer height provides valuable information on aerosol vertical structure and the degree of correspondence between surface and column aerosol conditions.

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.

Post‐processing techniques are widely used in renewable energy forecasting to improve prediction accuracy by reducing uncertainty caused by the intermittent nature of solar energy. This study investigates the effect of applying post‐processing at different stages of the solar irradiance‐to‐global tilted irradiance (GTI) conversion pipeline, specifically before (PP‐GTI) and after (GTI‐PP) GTI conversion, to determine which strategy provides greater improvements in forecasting performance. Using high‐resolution meteorological data from the Unified Model Local Data Assimilation and Prediction System (UMLDAPS) and satellite‐based irradiance estimates from UASIB‐KIER, these techniques were applied to solar forecasts for Daejeon and Jeju, South Korea. Performance was assessed using statistical indicators, box plots, and percent forecast correction metrics. The results demonstrate that the impact of post‐processing depends on both the selected method and its position within the GTI conversion framework. While GTI‐PP generally produced smoother forecast distributions and reduced variability for some methods, PP‐GTI also showed advantages in specific cases, particularly in forecast correction capability. Among the evaluated approaches, NN achieved higher correction levels with greater variability, KF provided more consistent performance, and analog ensemble (AnEn) demonstrated a balanced response between correction capability and stability. These findings indicate that the optimal placement of post‐processing should be determined based on the characteristics of the forecasting method and the desired balance between accuracy and reliability, providing practical guidance for improving solar forecasting strategies and photovoltaic (PV) system applications.

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.

The urgent need for sustainable energy solutions to combat climate change and the growing global energy demand has stimulated the development of renewable energy technologies. Among these, hybrid renewable energy systems (HRES) that combine multiple energy sources promise enhanced efficiency, reliability and flexibility compared to single-source systems. This research focuses on a novel HRES configuration that integrates a concentrated solar power named solar tower with thermal energy storage (TES), photovoltaic modules and battery energy storage systems (BESS) in North Togo. The study aimed to maximize the energy produced, while reducing costs and finally evaluated the environmental aspect saved. We use parametric optimization through SAM to determine the optimum levelized cost of energy and net present value. SAM software was used for heliostat modelling. The single-owner model was used for financial analysis. The results demonstrated the project's financial viability and environmental impact, with an LCOE of $0.14kWh-1 indicating a competitive cost of energy production and an IRR of 14.87% showcasing strong investment returns. A high NPV of $9,307,001 indicated some good interest in the proposed HRES. Furthermore, the significant reduction of 25,815.7513 tons of CO2 emissions highlights the project's substantial contribution to sustainability and carbon footprint reduction.

Power companies have found that solar irradiance forecasting is a reliable method for anticipating and preparing for the intermittent nature of renewable energy sources (RESs). However, when the percentage of RESs in the energy mix rises, there is a negative correlation between forecasting accuracy and process error, which could impact not only the grid but also the whole RES’s economic sustainability. In order to tackle this problem, this paper examines the economic implications of employing the Analog Ensemble (AnEn) forecasting model within Korea’s solar energy sector. The levelized cost of energy (LCOE) model and the revenue prediction model are applied to assess the efficacy of the AnEn. The proposed revenue scheme scheme was initiated on a 1 MW photovoltaic (PV) ground‐type power plant deployed in the mainland of South Korea. Various methods have been examined to assess the financial benefit of utilizing the AnEn, such as forecast verification and LCOE and revenue forecast scheme. Key findings reveal that the AnEn model consistently outperforms traditional models in terms of accuracy, particularly during critical solar peak months. This improved accuracy translates into more reliable predictions of solar irradiance, essential for minimizing overestimations and supporting realistic financial planning. Furthermore, the paper explores the economic incentives implemented by the Korean government to encourage precision in energy forecasting, including a structured incentive scheme tied to forecast accuracy.