## 2 Materials and Methods
### CEEMDAN-CNN-ATT-LSTM Model
This combination makes the model robust and allows it to generalize the time series to data with high variability, such as the high-altitude regions of southern Ecuador. In addition, the model is adaptable and can be applied to regions with more stable conditions. Our model obtained an encouraging performance, with an MAE of 99.89 W/m\({}^{2}\) in winter and 110 W/m\({}^{2}\) in summer for a 12 h forecast, covering the entire diurnal solar cycle. The model was found to perform better when the time series was less variable, highlighting the importance of the resampling approach used in this study. Although our study obtained promising results, we found some limitations that we will address in the future, such as the dependence on the quality and quantity of the data used. In addition, the computational complexity of our hybrid model requires advanced hardware, such as GPU and sufficient RAM, which may cause problems in resource-constrained environments. Moreover, variations in the availability and quality of data from different regions or time periods can also affect the generalizability and performance of the model. In the future, more advanced techniques will be adopted to optimize the speed and generalizability of the model, such as the use of transformer-like techniques. To this end, we will test its robustness in the case of stable and highly variable geographical regions and climatic conditions. In addition, we will include other related variables to increase the accuracy and reliability of the forecasting by adding weather forecasts and atmospheric parameters. Finally, such a model can be implemented within the operational structure, interfacing with real-time solar power generation systems, and be part of a grid connection with various performance optimization and adaptability options. Thus, the proposed approach will help the scientific community in the solar energy field to improve the methodological improvement of forecasting model building, with applicability in various scenarios to develop more accurate and efficient models in unpredictable and variable weather conditions. [PERSON], [PERSON] and [PERSON] contributed to the conceptualization and methodological design of the proposal, were responsible for data preparation and analysis, and prepared the original draft; [PERSON] contributed to the review and editing; and [PERSON] was responsible for project supervision. All authors have read and accepted the published version of the manuscript. This research was partially funded by the Junta de Comunidades de Castilla-La Mancha and the E.U. FEDER (SBPLY/23/180225/000226) and by the Spanish Ministry of Economy and Competitiveness and the European Union (PID2021-126082 OB-C21). Not applicable. Data Availability Statement: The data presented in this study are available in Station 50001--Faculty of Energy of the National University of Loja at [[https://unlmeteorologico.com/estacion-unl-facultad-de-energia/](https://unlmeteorologico.com/estacion-unl-facultad-de-energia/)]([https://unlmeteorologico.com/estacion-unl-facultad-de-energia/](https://unlmeteorologico.com/estacion-unl-facultad-de-energia/)) (accessed on 24 November 2024). For access to the base code, please contact the corresponding author via email. The authors would like to recognize the assistance received by National University of Loja through the research project 20-DI-FEIRNNR-2023. The authors declare no conflicts of interest. ## References

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