## 4 Conclusions  
This work addresses a significant gap in the research landscape of GHI prediction in Zacatecas, where such comprehensive investigations are scarce. By employing a variety of predictive models ranging from traditional regressors to advanced neural networks, we have democratized access to accurate GHI forecasting techniques. This diversity not only enhances the reliability of predictions but also provides stakeholders with a range of tools to make informed decisions regarding renewable energy strategies and agricultural planning in the region. Figure 6: Time series of 1-day-ahead GHI forecasted by the best and worst model and the corresponding measurements using the test data (2018). Figure 7: Model comparison for the test year (2018) using (**a**) r and (**b**) RMSE %. Moving forward, future research efforts in Zacatecas and similar regions would greatly benefit from access to longer and high-quality datasets. The limited availability of extensive and reliable data has been a significant challenge in this study, particularly affecting the accuracy of predictions under certain weather conditions such as cloudy days. Longer time series and improved data quality would enable models to capture more nuanced patterns and variations in solar radiation, thereby enhancing their predictive capabilities and robustness in diverse environmental scenarios. Expanding the forecast horizon for GHI prediction is a critical area for future work due to its potential to significantly enhance the planning and management of solar energy resources. While short-term forecasts (1-2 days ahead) are highly accurate and valuable for immediate operational decisions, extending the forecast horizon could provide substantial benefits for long-term energy planning, grid stability, and the integration of renewable energy into the power supply. For instance, accurate long-term solar radiation forecasts could enable better scheduling of maintenance for photovoltaic (PV) systems, optimization of energy storage solutions over longer periods, and improved forecasting of energy production for both utility-scale solar farms and distributed energy resources. Moreover, longer-term predictions could aid in strategic planning for energy trading and resource allocation, reducing the reliance on fossil fuels and enhancing the overall sustainability of the energy grid. However, extending the forecast horizon presents challenges, including the increased uncertainty and complexity of predicting GHI over longer periods. Future research should focus on developing advanced modeling techniques, such as hybrid models that combine machine learning with physical-based approaches, as well as improving data assimilation methods that incorporate real-time observations to continually refine and update predictions. By addressing these challenges, the development of reliable long-term forecasts could greatly enhance the utility and impact of solar energy forecasting, contributing to more efficient and sustainable energy systems. Moreover, the democratization of predictive models through this study highlights the potential for broader applications across various sectors in Zacatecas. By making these methodologies accessible and adaptable to local conditions, policymakers, energy analysts, and researchers can leverage these tools to optimize energy production, improve resource management, and foster sustainable development initiatives in the region. This approach not only supports informed decision-making but also lays the groundwork for future advancements in renewable energy technologies and climate adaptation strategies tailored to local needs. While this study provides valuable insights into GHI prediction methodologies in Zacatecas, it underscores the critical need for ongoing efforts to expand data collection initiatives. By addressing these challenges, future studies can build upon this foundation, advancing our understanding and capabilities in solar energy forecasting and environmental management in the region and beyond.