# Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning
## 1 Introduction  
In recent years, several forecasting methods have been developed for the output power of renewable energy sources (RES) [1], addressing in particular the intrinsic variability of parameters related to changing weather conditions, which directly affect the photovoltaic (PV) systems' power output [2]. This increasing attention is mainly due to the increasing shares of RES quota in power systems, which involve novel technical challenges for the efficiency of the electrical grid [3]. In particular, predictive tools based on historical data can generally provide advantages in PV plant operation [4, 5], reduce excess production, and take advantage of incentives for RES production [6]. Among the commonly-used forecasting models, most aim to predict the expected power production based on numerical weather prediction (NWP) systems forecasts [7]. This is a complex problem with high degrees of non-linearity; for this reason, it is commonly approached by means of advanced models and techniques--i.e., evolutionary computation [8], machine learning (ML) [9], and artificial neural networks (ANNs) [10]. These are pseudo-stochastic iterative approaches defined in the class of computational intelligence techniques, and are usually employed to address pattern recognition, function approximation, control, and forecasting problems [11]. Moreover, they are generally able to handle incomplete or missing data and solve problems with a high degree of complexity. Recently, several ANN layouts have been developed to solve different tasks [12], such as: times series prediction, complex dynamical system emulation [13], speech generation, handwritten digitrecognition, and image compression, due to their ability to learn from extended time series of historical measurements with acceptable error levels compared to other statistical and physical forecasting models [14]. Currently, ANN employment in forecasting is quite straightforward due to the widespread development of specific software applications [15; 16; 17]. In particular, the first attempts at solar power forecasting by means of ANN started more than a decade ago [18]. Generally, in the case of PV power output, common training data are the historical measurements of power production from a PV facility and meteorological parameters unique to the facility location, including temperature, global horizontal irradiance (i.e., the intensity of all the solar radiation components on a horizontal surface) [19], and cloud cover above the facility. Additional forecasted variables from the numerical weather predictions can also be considered, such as wind speed, humidity, pressure, etc. [20]. Novel forecasting models were recently implemented by adding an estimate of the clear sky radiation to the series of historical local weather data, as reported in [21]. Additionally, the effectiveness of ensemble methods was demonstrated in [22], thus giving additional advantages in terms of results reliability and the implementation of efficient parallel computing techniques. In their previous work [23], the authors conducted a detailed analysis to find a procedure for the best ANN layout and settings in terms of the number of layers, neurons, and trials for the PV day-ahead forecast. Furthermore, evidence showed that the forecasting performance of ML techniques is affected by the composition of the training data-set, as well as by input selection [24; 25]. In this paper, a specific study is conducted on training data-sets in order to provide a more detailed analysis of the effect of different approaches in the training data-set composition on the day-ahead forecast of the PV power production. In particular, the authors present some procedures to set-up the training and validation data-sets for the ANN used in physical hybrid method to perform the day-ahead PV power forecast in view of the electricity market.