## 1 Introduction  
The global shift toward renewable energy sources has become an imperative in the face of escalating climate change and the urgent need to reduce greenhouse gas emissions. Among the various renewable technologies, Solar Photovoltaic Systems (SPVSs) have emerged as a cornerstone of sustainable energy solutions worldwide. Their capacity to harness an abundant and renewable resource--sunlight--positions them as a vital player in the transition from fossil fuels to cleaner energy alternatives. This transition is not only crucial for environmental preservation, but also for enhancing energy security and economic resilience in an era characterized by fluctuating fossil fuel prices and geopolitical uncertainties. As the demand for clean energy continues to grow [1], SPVSs offer numerous advantages, including scalability, low operational costs, and adaptability across diverse environments, from residential rooftops to large-scale solar farms. These systems can be deployed in various geographical contexts, enabling a wide range of stakeholders--fromindividual homeowners to large corporations and governments--to participate in the renewable energy landscape [2]. However, the optimization of their performance and reliability remains a significant challenge, primarily due to the intricate interactions between environmental factors, such as temperature, dust accumulation, and solar irradiance, and operational conditions, including system design and maintenance practices [3]. The impetus for this research arises from the critical role that Renewable Energy Systems (RESs) must play in meeting the world's increasing energy demands while simultaneously reducing carbon footprints. Effective modeling of SPVS performance is essential for identifying potential inefficiencies and enhancing overall reliability. In fact, advanced predictive models allow stakeholders to enhance energy yield predictions, reduce downtime, and lower operational risks, which helps protect their financial investments in solar projects. Additionally, strong modeling techniques support better decision-making, which, in turn, ensures that solar installations remain sustainable and successful over the long term. This research is particularly relevant at both national and international levels. Countries rich in solar resources, such as Oman, are striving to expand their renewable energy share to meet climate goals and lessen their dependence on fossil fuels. The success of these initiatives is heavily dependent on the efficiency and reliability of SPVSs, which are inherently tied to sophisticated performance modeling. Furthermore, fostering international collaboration and data sharing within this domain could drive innovations, standardize methodologies, and promote best practices, thereby facilitating the widespread adoption of solar energy technologies that are both financially viable and environmentally sustainable. Moreover, recent advances in data science and machine learning (ML) present new opportunities for enhancing the predictability and intelligence of SPVS performance models [4; 5; 6]. This paper explores the application of four prominent regression techniques--Linear Regression (LR), Lasso Regression (LAR), Ridge Regression (RR), and Elastic Net Regression (ENR)--each tailored to address the complexities of SPVS data. Linear Regression offers a foundational understanding of the linear relationships between independent variables and energy output. LAR introduces a penalty on coefficient size, facilitating effective variable selection. RR addresses multicollinearity, leading to more stable predictions, while ENR strikes a balance between variable selection and regularization. Utilizing a comprehensive dataset from the Shams Solar Facility at the German University of Technology in Oman, which comprises 36,851 observations of various environmental and operational parameters, this study aims to develop robust models for forecasting energy output.