## 1 Introduction  
Renewable energy (RE) will play a significant role in the future energy structure as one of the ways that humanity may address the issue of fossil fuels [1]. Because it is widely available and sustainable, solar energy is regarded as one of the most promising RE sources [2]. A significant portion of RE power generation comes from solar power generation, particularly photovoltaic (PV) power generation. The great potential for energy productivity of PV energy systems makes them one of the most popular and desired RE technologies [3]. Therefore, as the use of PV power-generating systems increases, so does the importance of ensuring reliability in PV power systems [4]. As the capacity of PV systems increases, the risks associated with controlling the power generated also increase [5]. Therefore, studies to evaluate and improve the performance of solar energy systems are crucial. The power output evaluation studies of PV modules can be performed by obtaining the I-V (current-voltage) curve with solar simulators that provide the Standard Test Conditions specified in American Society for Testing and Materials (ASTM) 927-10 (a standard radiation spectrum of 1000 W/m\({}^{2}\), a cell temperature of 25 \({}^{\circ}\)C, and an air mass of AM1.5) [6]. However, forecasting studies have emerged since this method cannot be applied in PV power plants, where meteorological conditions vary significantly. Numerous studies have demonstrated that local climate zones and meteorological conditions significantly impact the amount of solar energy [7]. The fluctuation in PV power generation is determined by meteorological conditions, including temperature, wind speed and direction, relative humidity, cloud cover, and solar radiation [4; 8]. Regarding PV power system operation, planning, and marketing, the unpredictability and fluctuation of these weather variables pose significant challenges to stability, dependability, and integration [9]. Therefore, PV power forecasting has become essential for economic load allocation and system functioning [10]. A highly accurate forecast can lower operating costs, increase system operational efficiency, and enhance the performance of the system's contingency analysis [11]. Integrating PV power into modern energy systems requires accurate forecasting to ensure grid stability and efficient energy management. However, irrelevant data in meteorological datasets for PV power estimation poses significant challenges, including model inefficiency, computational burden, and reduced forecasting accuracy. Many meteorological variables, although readily available, do not significantly contribute to PV power prediction, resulting in data redundancy and noise in forecasting models [12]. Unfiltered and excessive data inputs can obscure the relationships between key meteorological factors, such as solar irradiance, temperature, and wind speed, and PV power output, thereby complicating the development of robust predictive models. One of the primary issues caused by unnecessary data is the increased dimensionality of datasets. When irrelevant or weakly correlated meteorological parameters are included in forecasting models, they increase computational complexity without improving prediction accuracy. High-dimensional data can slow down model training, resulting in longer processing times and requiring more computational resources [13]. Additionally, deep learning (DL) models trained on excessive features may suffer from overfitting, where the model learns noise and irrelevant fluctuations instead of meaningful trends, ultimately reducing generalization performance when applied to new data [14]. Another major concern is the introduction of collinearity between meteorological variables, which can distort the estimation of their respective impacts on PV power generation. For example, multiple atmospheric parameters, such as humidity, cloud cover, and air pressure, may exhibit strong correlations, leading to redundancy and reducing the interpretability of machine-learning (ML) models [15].