# Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model
## 1 Introduction  
With the increase in the proportion of solar energy in the electrical grid, the challenges linked to its integration have become more noticeable. The fluctuating nature of solar energy poses a considerable challenge, as variability in its output can cause problems such as power surges or grid congestion [1]. These problems are intensified by the lack of access to high-quality data, particularly those related to relevant climate information, which is essential to increase the accuracy of photovoltaic generation predictions [2]. The lack of adequate data restricts the development of new methodologies that could improve planning and facilitate more efficient integration of solar energy into electrical systems, helping to mitigate the technical drawbacks derived from its massive expansion [3, 4]. Accurate solar energy prediction not only plays a crucial role in enhancing the seamless integration of solar power systems with the electrical grid, which in turn boosts overall efficiency, but it also serves as a key enabler for implementing proactive maintenance strategies. By proactively identifying potential issues before they manifest, these strategies play a pivotal role in minimizing both operational downtime and the associated repair expenses. This proactive approach proves exceptionally advantageous in solar farms, where it not only optimizes operational efficiency but also significantly contributes to prolonging the lifespan of solar equipment, thereby reducing operational costs and enhancing its performance in a simultaneous fashion [5, 6]. There are mainly three approaches to photovoltaic generation forecasting: physical, based on theoretical models; statistical, using time-series analysis; and hybrid methods,which combine both [7]. Although each approach has its advantages and limitations, there is still no agreement on which is more accurate [8]. Physical methods, while accurate, require detailed plant metadata [9]. Statistical methods, although flexible, may lack precision if the parameters of the photovoltaic plant are not fully understood [10]. One of the principal quantities in the process of converting solar resources into photovoltaic power is Global Horizontal Irradiance (GHI). This quantity can be measured using atmospheric instruments installed in meteorological stations located in situ at solar plants or estimated through climate numerical models. For photovoltaic power modeling, there are various methods which can be classified into direct methods (or data-driven), indirect methods (which model the physical process of converting GHI into power), and hybrid methods, which are a combination of the previous ones. The direct approach considers the output power of a photovoltaic system as a dependent variable, while irradiance and other meteorological variables serve as the independent variables. In contrast, the indirect approach explicitly considers the physics of the different conversion steps, which include solar positioning, separation modeling, transposition modeling, photovoltaic cell temperature modeling, soiling, shading, mismatch, and degradation, among others [11]. This study proposes a hybrid methodology focused on an intra-hour forecast of photovoltaic power generation. Initially, public data sources will be evaluated to ensure the reproducibility of the research. Subsequently, the state of the art related to the selected dataset will be examined. Based on this review, we propose to use the Transformer Bi-LSTM model for the forecasting tasks and evaluate using two methodologies: one direct and the other inspired by the traditional physical model chain, but where each block of the chain will be replaced by the aforementioned model, leveraging the advantages of transfer learning. This is defined as the ability to improve the learning of a target predictive function in a target domain by utilizing knowledge acquired in one or more source domains. These source domains may have similar or different learning tasks and may share the same or different feature spaces [12; 13].