###### Abstract
proposed an artificial neural network (ANN) for the long-term prediction of an existing 1 MWp plant using three different options. Input variables were the module's ambient temperatures, wind speed, and global irradiance. The ANN model with the four inputs achieved more accurate results with a mean absolute percentage error (MAPE) of 1.68% [12]. [PERSON] et al. proposed the application of the seasonal autoregressive integrated moving average (SARIMA) model and two ANNs for energy generation forecasting of grid-connected plants in Greece. Comparisons were conducted in terms of normalized root mean square error (nRMSE) [13]. [PERSON] et al. created an ANN to predict power from a real PV plant in Italy. Three scenarios were investigated: data employed for both training and prediction, data employed just for training and prediction was compared with data from the global data assimilation system, and finally, data from GDAS were used for training and prediction. The results show that the lack of on-site weather measurements can be faced by means of a validated numerical weather model in combination with machine learning techniques for PV power forecasting [14]. [PERSON] et al. proposed a forecasting model based on the long short-term memory (LSTM) algorithm. The examined parameters were solar irradiance, PV module temperature, historical PV data, and clearness index. The results indicated that the inclusion of the clearness index as input can improve the performance of the forecaster [15]. [PERSON] et al. studied PV power production data of a 5.94 kWp grid-connected PV plant in Morocco. The PV system comprised panels of three different PV technologies. The proposed models are an ANN and a persistence model that are applied to a data set with solar irradiance and temperature as inputs. The results were compared in terms of mean bias error (MBE), mean absolute error (MAE), MAPE, RMSE, and nRMSE. The ANN-based model demonstrated a 2.107% MAE and 2.645% RMSE against 2.406% and 5.185%, respectively, for the persistence model [16]. [PERSON] et al. proposed a model for an hour-ahead prediction on a yearly basis of three different PV plants, based on available data for wind speed, module, ambient temperature, and solar irradiation employing a long short-term memory (LSTM) recurrent neural network (RNN) with a deep learning method, with the results compared with regression, hybrid Adaptive neuro-fuzzy inference system (ANFIS), and machine learning methods [17]. [PERSON] et al. proposed a radial belief neural network (RBFN) with inputs from large-scale PV plants with evaluation metrics of RMSE, nRMSE, MBE, MAE, MaxAE, MAPE, and Kolmogorov-Smirnov test integral (KSI) and OVER metrics, skewness, and kurtosis and variability estimation [18]. [PERSON] et al. proposed a support vector machine (SVM) that uses weather temperature, relative humidity, global horizontal radiation, diffuse horizontal radiation, wind direction, and sampling time as input parameters. The output of the model was active power and the results were a correlation coefficient R\({}^{2}\) up to 0.997, MSE and MAE values of 0.0349 and 0.1569, respectively [19]. [PERSON] et al. proposed a machine learning technique using latitude, month, hour, ambient temperature, pressure, humidity, wind speed, and cloud cover as independent variables and power production as output. A distributed random forest regression algorithm modeled the combined dataset with an R\({}^{2}\) value of 0.94 [20]. [PERSON] et al. presented a prediction model for comparing the performance of six different photovoltaic (PV) modules using artificial neural networks (ANNs), with the basic characteristics of the PV panels' manufacturer datasheets as cell temperature, irradiance, fill factor, short circuit current, open-circuit voltage, maximum power. Comparing the results to the actually measured data resulted in a 0.874% MAPE [21]. [PERSON] et al.