# Efficient Machine Learning-Based Prediction of Solar Irradiance Using Multi-Site Data
## 1 Introduction  
Climate change, driven by global warming, has become one of the most pressing challenges of our time. In response, nations worldwide have committed to seeking effective solutions to mitigate its impact [1]. A landmark step in this direction was the Paris Agreement of 2015, which set a critical objective: limiting the rise in global average temperature to well below 2 \({}^{\circ}\)C above pre-industrial levels, with an aspirational target of 1.5 \({}^{\circ}\)C. Achieving these ambitious goals requires a fundamental shift toward renewable energy sources, particularly green energy technologies that can replace fossil fuels [2]. Among these, solar energy stands out as one of the most promising and abundant resources available. Solar energy's potential hinges on the efficient utilization of solar irradiance--the power per unit area received from the sun through electromagnetic radiation. Solar irradiance plays a pivotal role in optimizing the performance of solar panels and other solar energy systems. Accurate forecasting of solar irradiance is essential for maximizing the efficiency of photovoltaic (PV) systems and ensuring reliable integration into the power grid. Among the various parameters of solar irradiance, predicting global horizontal irradiance (GHI) --the total amount of solar radiation received on a horizontal surface--is the first and most crucial step in most PV power prediction systems [3]. The prediction of GHI has been widely studied in the literature, with researchers proposing a variety of methods to address the challenges of accuracy, scalability, and generalization [4]. Table 1 summarizes the key approaches and their classifications. Recent advancements in machine learning (ML) have opened new avenues for improving GHI forecasting, making it a focal point of this study. For instance, in [5], the authors introduced a novel solar irradiance forecasting method designed to optimize photovoltaic generation planning and management in smart grids. Their approach combined long short-term memory (LSTM) models with the Choquet integral for aggregation, achieving superior accuracy compared to traditional methods such as ARIMA and persistence models. The success of this method highlights the importance of modeling temporal changes and capturing interactions between predictions. In [11], researchers tackled the challenge of forecasting solar irradiance in locations lacking historical data by leveraging metric learning techniques. By using irradiance  
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measurements from sites with similar radiation patterns, they demonstrated that accurate models could be trained even in data-scarce regions. When combined with clear-sky models and relevant meteorological data, this approach significantly improved forecasting accuracy, offering a practical solution for deploying solar PV systems in new locations. Another innovative approach was presented in [12], where real-time surface irradiance mapping models were used to forecast minutely solar irradiance under varying cloud conditions. By extracting RGB values and position information from sky images, the authors achieved high forecasting accuracy across different cloud types, including blocky, thin, and thick clouds. This method underscores the potential of real-time data processing in addressing rapid fluctuations in solar irradiance. In [13], the authors presented Gaussian Process Regression (GPR) as a new accurate soft computing model to predict daily and monthly solar radiation in Mashhad, Iran, using meteorological data from 2009 to 2014. They employed sensitivity analysis to identify the most relevant input features and evaluated the model using metrics such as mean absolute percentage error (MAPE), root mean square error (RMSE), and efficiency factor (EF). The aim was to develop a reliable prediction model for solar radiation using minimal and relevant meteorological parameters.