## 4 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Forecasting Sensors/Method** & \multicolumn{2}{c}{**Strengths**} & \multicolumn{2}{c}{**Weaknesses**} \\ \hline Ground-Based Sensor & \(\bullet\) & High temporal resolution. & \(\bullet\) & Limited spatial coverage. \\ Measurements & \(\bullet\) & Effective for very short-term forecasting. & \(\bullet\) & High maintenance and \\  & \(\bullet\) & Real-time data. & \(\bullet\) & calibration costs. \\ \hline Satellite Data Processing & \(\bullet\) & Extensive spatial coverage. & \(\bullet\) & Lower temporal resolution. \\  & \(\bullet\) & Suitable for medium to & \(\bullet\) & Susceptibility to cloud cover and \\  & & long-term forecasts. & \(\bullet\) & atmospheric conditions. \\ \hline All-Sky Camera Images & \(\bullet\) & High-resolution images. & \(\bullet\) & Limited to camera coverage area. \\  & \(\bullet\) & Enables very short-term forecasts. & \(\bullet\) & High initial setup costs. \\  & \(\bullet\) & Real-time detection of cloud cover. & \(\bullet\) & Reduced accuracy in adverse \\ \hline Statistical Regression & \(\bullet\) & Simple implementation. & \(\bullet\) & Heavy reliance on historical \\ Approaches & \(\bullet\) & Effective trend identification with & \(\bullet\) & Limited adaptability to sudden \\  & & historical data. & \(\bullet\) & weather changes. \\ \hline Artificial Intelligence (AI) Methods & \(\bullet\) & Handles large datasets. & \(\bullet\) & High computational resource \\  & \(\bullet\) & Identifies complex patterns. & \(\bullet\) & Need for specialized expertise and \\  & \(\bullet\) & Accuracy improves over time. & \(\bullet\) & potential opacity in \\ \hline Numerical Models & \(\bullet\) & Detailed atmospheric simulations. & \(\bullet\) & Computationally intensive. \\  & \(\bullet\) & Robust for medium to & \(\bullet\) & Accuracy depends on initial \\  & & long-term predictions. & & conditions and model resolution. \\ \hline Hybrid Methods & \(\bullet\) & Combines multiple data sources. & \(\bullet\) & Complex integration. \\  & \(\bullet\) & Enhances accuracy. & \(\bullet\) & High computational demands. \\  & \(\bullet\) & Reduces uncertainties. & \(\bullet\) & Requires multi-disciplinary \\  & & & expertise. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Critical analysis of solar energy forecasting sensors/methods. ground-based sensors, satellite data, and all-sky cameras with AI and numerical models, shows potential for reducing uncertainties and enhancing forecast robustness. Advanced image processing techniques, particularly those utilizing deep learning algorithms, are improving the interpretation of satellite and all-sky camera images, enabling more precise detection of cloud movements and other atmospheric phenomena affecting solar irradiance. The incorporation of real-time data assimilation techniques, which continuously update forecasting models with the latest observational data, also enhances short-term forecast accuracy by capturing sudden changes in weather conditions. Furthermore, the deployment of Internet of Things (IoT) devices and extensive sensor networks is providing high-resolution, real-time data across larger spatial areas, facilitating more localized predictions. Lastly, blockchain technology is emerging as a secure and transparent method for managing and sharing forecasting data, enhancing data integrity, facilitating collaboration among stakeholders, and ensuring the reliability of data used in forecasting models. These trends represent significant advancements in the field of solar energy forecasting, and their continued development and integration into existing systems are expected to address current limitations and drive further improvements in forecast accuracy and reliability. Finally, the distribution of studies by country, shown in Figure 4, indicates that research activity is most intense in the United States and several European nations, with a notable presence also in China, Japan, and Australia.