## 1 Introduction
Factors such as temperature fluctuations, humidity, and exposure to pollutants can contribute to the degradation of the materials used in photovoltaic (PV) panels, which can result in a reduction in efficiency. For example, prolonged exposure to elevated temperatures can cause thermal cycling, which can result in the delamination of layers within panels and the formation of microcracks [6]. To mitigate these effects and ensure the longevity and efficiency of PV systems, regular monitoring and maintenance are essential. In addition to the environmental factors previously discussed, the geographical location of PV installations can also have a significant impact on their efficiency. Regions with elevated levels of solar radiation and favorable climatic conditions are more likely to achieve higher energy outputs from photovoltaic (PV) systems. Conversely, regions with high levels of cloud cover, high humidity, or extreme temperatures may experience a reduction in efficiency [4]. Consequently, the choice of location is of paramount importance in the deployment of PV systems, as it can have a significant effect on their overall performance and energy production capacity. Various approaches to maximizing PV panel output power performance have been presented in the literature. [PERSON] et al. studied an M-shaped configuration of PV panels and showed that this arrangement can increase the efficiency of solar energy production compared to traditional S-shaped configurations [7]. An artificial neural network (ANN) model was developed by [PERSON] and [PERSON], trained on real performance data, for the purpose of predicting the output of a grid-connected photovoltaic (PV) park. Intheir research, they emphasized the ability of ANNs to separate the effects of pollution on PV panels [8]. [PERSON] and [PERSON] examined the utility of non-linear autoregressive neural networks (NARXs) and deep feedforward neural networks (DFFs) with exogenous inputs for modeling the maximum output power of photovoltaic (PV) panels. Their comparative analysis demonstrated that different neural network architectures can yield varying degrees of accuracy in predicting PV output, thereby suggesting the possibility of employing specialized approaches in performance modeling [9]. In the existing literature, numerous environmental factors have been incorporated into machine learning regression models at the regional level. [PERSON] et al. [10] achieved an optimal result with a linear regression model for solar energy estimation, whereas [PERSON] et al. [11] used Support Vector Regression with temperature, dew point, wind speed, cloud cover, and visibility data for irradiance estimation. In their study, [PERSON] et al. [12] employed a range of estimation models, including Lasso, Ridge, Linear, and Support Vector Regression (SVR), to predict solar energy output. [PERSON] and colleagues [13] employed a Random Forest model to predict PV power (relative humidity, wind speed and direction, horizontal irradiance, relative horizontal irradiance, temperature). The efficiency of photovoltaic (PV) panels is inextricably linked to a multitude of environmental factors. It is crucial to comprehend these influences in order to enhance the efficiency of PV systems and guarantee their long-term reliability as a renewable energy source. In this study, the energy outputs of 100-watt polycrystalline and monocrystalline PV panels were monitored during the initial six months of the year, with the data obtained subjected to analysis using six distinct machine learning regression models. This allowed for a comparison of the predictive capabilities of various PV panel power output models.