## 1 Introduction  
The transition towards renewable energy is a topic of significant relevance, with solar energy standing out as a promising option. However, the challenge of intermittency in its generation persists, which is evident in technologies such as photovoltaic panels [1]. Additionally, climatic and geographic factors hinder the effective integration of solar energy into conventional electrical grids [2, 3]. To overcome these challenges, innovative solutions are being researched and developed, such as algorithms based on artificial intelligence for energy management, to improve solar energy's reliability and efficiency as a primary renewable energy source [4]. In the city of Zacatecas, Mexico, research on solar radiation is limited due to several factors, despite its significant energy potential. The primary limitations include a lack of comprehensive and long-term data on solar radiation, which restricts the ability to develop and validate accurate predictive models. The lack of comprehensive studies on this topic in the region highlights the need to develop prediction models that consider local climate variability, such as cloud cover, incident radiation, and the position of the sun [5]. These models are essential to accurately estimate the amount of solar radiation available during specific periods, which is crucial to optimize the efficiency of photovoltaic systems and improve the management of solar energy generation and storage in Zacatecas [6]. This approach can contribute significantly to the development of a sustainable and resilient energy sector in the region, despite the limited previous research in this area. Currently, there is a trend towards the construction of new photovoltaic power plants in the region. However, they have do not usually take advantage of innovative technologies, such as neural networks, to improve the accuracy of predictions. Neural networks are designed to analyze complex and non-linear data. They have the potential to capture local weather patterns and generate reliable forecasts of solar radiation in the city of Zacatecas [7]. By integrating historical daily Global Horizontal Irradiance (GHI) data that are specific to this area into models based on neural networks, it is possible to obtain a detailed and precise estimation of solar radiation, which is crucial for strategic planning and decision-making in the solar energy sector of the region [8]. Harnessing the power of neural networks could create new opportunities to improve the efficiency and profitability of solar energy systems in Zacatecas. The use of specialized models represents a promising innovation to address problems in predicting solar radiation [9]. This approach, based on machine learning and time series techniques, has proven effective in various fields and offers the ability to capture complex patterns in climate data [10]. However, successful implementation of the model requires access to accurate climate data and continuous validation of the results to ensure its reliability and utility in the region [10] and to address the challenge of accurately predicting solar radiation. This work explores the use of 15 different models, ranging from traditional statistical approaches to advanced machine learning techniques, to provide a comprehensive evaluation of their predictive capabilities. The selection includes persistence models, artificial neural networks (Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Evolutionary Neural Architecture Search (ENAS)), linear regressors, random forests, XGBoost, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), extra trees, AdaBoost, ElasticNet, Ridge, Lasso, and Meta's Prophet. Each model brings unique strengths: for example, neural networks are well-suited for capturing non-linear relationships, while ensemble methods like random forests and XGBoost are robust against overfitting [11; 12; 13].