Currently, the world is actively responding to climate change problems. There is significant research interest in renewable energy generation, with focused attention on solar photovoltaic (PV) generation. Therefore, this study developed an accurate and precise solar PV generation prediction model for several solar PV power plants in various regions of South Korea to establish stable supply-and-demand power grid systems. To reflect the spatial and temporal characteristics of solar PV generation, data extracted from satellite images and numerical text data were combined and used. Experiments were conducted on solar PV power plants in Incheon, Busan, and Yeongam, and various machine learning algorithms were applied, including the SARIMAX, which is a traditional statistical time-series analysis method. Furthermore, for developing a precise solar PV generation prediction model, the SARIMAX-LSTM model was applied using a stacking ensemble technique that created one prediction model by combining the advantages of several prediction models. Consequently, an advanced multisite hybrid spatio-temporal solar PV generation prediction model with superior performance was proposed using information that could not be learned in the existing single-site solar PV generation prediction model.

Precise and accurate prediction of solar photovoltaic (PV) generation plays a major role in developing plans for the supply and demand of power grid systems. Most previous studies on the prediction of solar PV generation employed only weather data composed of numerical text data. The numerical text weather data can reflect temporal factors, however, they cannot consider the movement features related to the wind direction of the spatial characteristics, which include the amount of both clouds and particulate matter (PM) among other weather features. This study aims developing a hybrid spatio-temporal prediction model by combining general weather data and data extracted from satellite images having spatial characteristics. A model for hourly prediction of solar PV generation is proposed using data collected from a solar PV power plant in Incheon, South Korea. To evaluate the performance of the prediction model, we compared and performed ARIMAX analysis, which is a traditional statistical time-series analysis method, and SVR, ANN, and DNN, which are based on machine learning algorithms. The models that reflect the temporal and spatial characteristics exhibited better performance than those using only the general weather numerical data or the satellite image data.

This study introduces a lightweight hybrid solar PV generation prediction model operating on 1-hour intervals, utilizing remote sensing data to enhance power grid management. Multisource remote sensing data, including spatial features from infrared satellite images and temporal data from various hourly recorded datasets, capture spatiotemporal characteristics. The model defines and synthesizes regions of interest (ROI) and surrounding areas of ROI (ROI _surr ) within satellite images to reduce computational load. Integration of image and numerical weather prediction (NWP) process modules ensures accurate prediction. Comparative analysis against five machine learning algorithms shows significant improvements, with up to a 33.7% decrease in mean absolute error (MAE) and a 19.51% decrease in root mean square error (RMSE). Additionally, the model consistently meets ASHRAE Guideline 14 standards and outperforms single-source data models. Experimentation highlights the effectiveness of smaller ROIs in enhancing predictive accuracy, demonstrating adaptability to climate variations. This lightweight multisource remote sensing-based hybrid model promises to guide smart grid operations and sustainable power grid systems, advancing remote sensing applications in renewable energy management.

Solar photovoltaic (PV) generation is a cornerstone of sustainable energy production, but predicting its capacity across countries remains challenging due to factors like climate, terrain, and population density. To address this, a recent study proposed a novel approach using transfer learning, which is particularly valuable when historical data for newly established PV plants is limited. The study evaluated four PV plants in South Korea and Germany, selected for their diverse geographical and climatic conditions. The proposed CL-Transformer model outperformed established machine learning models such as LSTM, CNN-LSTM, and Transformer, consistently demonstrating superior predictive capabilities. Notably, when trained on Korean data and applied to both South Korea and Germany, the model achieved an average R² _adj improvement of 23.5 %. When trained on German data, the improvement was even more pronounced at 67.3 %. Additionally, transfer learning experiments revealed up to a 50.6 % enhancement in R²adj across different plant scales. By integrating external weather variables and satellite data, this hybrid model provides valuable insights for accurate capacity prediction and strategic planning in deploying new PV plants, contributing to greater stability and efficiency in the power industry.

This study presents a strategy recommendation methodology for the optimal operation of photovoltaic-battery energy storage systems (PV-BESS) by leveraging large language models (LLMs) through in-context learning (ICL) and prompt engineering. Unlike traditional rule-based approaches, the proposed method effectively integrates electricity tariffs, solar PV generation, load, battery degradation, and policy constraints to generate context-specific operational strategies automatically. A 10-year simulation of a South Korean campus building indicated that the LLM-based approach reduced cumulative operating costs by 5–12% and lowered life cycle cost (LCC), including initial investment, replacement, and residual value, by 3–5%, demonstrating long-term economic viability. GPT-2 achieved reduced battery degradation through conservative operation, whereas LLaMA 3–8B improved PV utilization and renewable energy certificate (REC) revenue via diversified strategy selection. The findings confirm that LLM-based methods can be developed into intelligent operational systems that simultaneously optimize economic performance, efficiency, and sustainability, with potential applicability to distributed energy resource management and smart grid operations.

Forecasting of Photovoltaic(PV) power generation plays an important role in the planning and operation of the supply and demand response of the entire power grid system. Therefore, this study aims to develop an integrated model for predicting PV power generation in South Korea for stable and efficient power supply and demand response. Especially, in the case of PV power generation, it is important to increase the reliability of the prediction model for accurate prediction because it reacts sensitively to external factors such as atmospheric factors. Most of the previous studies focused on the atmospheric factors in the forecasting of the PV power generation. However, this study considers new parameters such as geographical and facility factors along with PV output of previous time step. Conventional time series method(SARIMAX) and machine learning-based methods, such as Support Vector Regression(SVR) and Artificial Neural Network(ANN) were employed for forecasting PV power generation.