###### Abstract  
Increased penetration of grid-connected PV systems in modern electricity networks induces uncertainty factors to be considered from several different viewpoints, including the system's protection and management. Accurate short-term prediction of a grid-connected PV park's output is essential for optimal grid control and grid resilience. Out of the numerous types of models employed to this enduring the last decade, artificial neural networks, (ANNs) have proven capable of handling the uncertainty issues of solar radiation. Insolation and ambient, or panel temperature, are most commonly employed as the independent variables, and the system's output power is successfully predicted within 3 to 5% error. In this paper, we apply a common type of ANN for the long-term prediction of a 100 kWp grid-connected PV park's output, by exploiting experimental data from the last 8 years of operation. Solar radiation and backsheet temperature were utilized for the ANN training stage. The performance metrics of this model, along with a standard linear regression model, are compared against the actual performance data. The capabilities of the ANN model are exploited in the effort to decouple the fluctuating effect of PV panel soiling which interferes with the efficiency degradation process. The proposed methodology aimed to quantify degradation effects and is additionally employed as a fault diagnosis tool in long-term analysis. eurm  
boosted the expansion of PV applications in the residential sector. Its application varieties include net-metering, virtual net-metering, and net-metering with battery systems in order to achieve zero energy induction in the public grid. Sizing these applications demands meteorological data, consumption data and profiles, and, most importantly, reliable solar potential data because of the uncertainty of the solar insolation. Thus, forecasting power output is an essential task to support the optimal sizing of these applications. The significant expansion of electric vehicles is another application that will necessitate a close synergy with rooftop and other PV installations [4]. Again, the system's optimization in these cases needs to be supported by reliable power forecasting because of the inherent uncertainty of the availability of solar energy and the randomness of the EV charging behavior [5]. Furthermore, power forecasting of the various forms of power production is also an important tool in electricity markets and energy markets [6; 7]. Many researchers face this problem using different approaches. [PERSON] et al. proposed an empirical technique using experimental data and interpolated datasheets' information in order to produce a mathematical equation for power prediction under various weather conditions [8]. [PERSON] et al. presented a method for energy rating using mathematical models for shallow-angle reflectivity, spectral sensitivity, module efficiency dependence on irradiance, and module temperature [9]. [PERSON] et al. studied PV forecasting for some power plants in mainland France using statistical methods exploiting outputs from numerical weather prediction (NWP) models. Forecasts were built without using technical information on the power plants. The results showed a root mean square error (RMSE) in the range of 9-12% [10]. Neural networks are another popular approach adopted by researchers in PV energy forecasting. [PERSON] et al. [6] presented the most recent research works on deep neural networks applied in the short term (forecast horizon from 30 min to 1 week ahead) photovoltaic power forecasting. [PERSON] et al. conducted a comparative analysis among three models for energy forecasting: a phenomenological model, a multilayer perceptron (MLP) neural network, and one based on regression analysis, applied to a dataset of an existing PV power plant. The neural networks technique demonstrated superior accuracy in the results [11]. [PERSON] et al.