# ANN Sizing Procedure for the Day-Ahead Output Power Forecast of a PV Plant
## 1 Introduction  
The recent increase of renewable energy sources (RES) quota in power systems is facing new technical challenges, involving also the overall efficiency of the electrical grid [1]. Therefore, several techniques able to forecast the output power of RES facilities have been developed, in particular addressing the inherent variability of parameters related to solar radiation and atmospheric weather, which directly affect photovoltaic (PV) systems' power output [2]. Robust predictive tools based on the measured historical data of a specific PV plant can provide advantages in its operation policy, in order to avoid or minimize excess production, and to benefit from the available incentives for producing electricity from RES [3]. Moreover, the increasing penetration of accurate forecasting models, constrained by the market and incentives, can implicitly facilitate the alignment of producers' decisions with energy policy and emissions targets. In particular, these predictive models are aimed at finding out the relationship between the numerical weather prediction and the forecasted power output [4]. However, any forecasting activity of natural phenomena is characterized by a high degree of non-linearity. To address this complexity, artificial neural networks (ANNs) with a multilayer perceptron (MLP) architecture are often used. These are a class of machine learning (ML) techniques used to solve specific kinds of problems, such as: pattern recognition, function approximation, control, and forecasting [5; 6; 7]. ANNs are known as a very flexible tool, and several layouts have been developed to solve different tasks [8]. In particular, ML forecasting methods can be used on an extended dataset of historical measurements, providing an advantage in reducing prediction errors with respect to other statistical and physical forecasting models [9; 10]. An additional advantage of ML techniques is their ability to handle missing data and to solve problems with a high degree of complexity. The first breakthroughs in using ML technologies in the solar power forecasting field were pioneered more than a decade ago [11]. A comprehensive review of solar power forecast approaches is given in [12]; this paper provides a systematic discussion comparing and contrasting many works on applying artificial neural networks to the PV forecasting problem. ML techniques are demonstrated to be successfully applied for this purpose, provided that suitable historical patterns are available. In addition, the potential benefits of hybrid techniques, such as those in [13; 14], are also thoroughly discussed. Nowadays, in the case of a particular PV plant's power output, the common training data are the historical measurements of power production, and the meteorological parameters related to the specific plant's location, which include temperature, global horizontal irradiance, and cloud cover above the facility. Additional forecasted variables from numerical weather predictions can also be considered, such as wind speed, humidity, and pressure. Recently, novel forecasting models have been implemented by adding an estimate of the clear sky radiation to the series of historical local weather data, in order to hybridize the meteorological forecasts with physical data, as reported in [15]. Moreover, the effectiveness of using ensemble methods, i.e., running simultaneously a number of parallel neural networks and finally averaging their results, was demonstrated in [16], thus giving additional advantages also to modern parallel computing techniques. The same paper also shows the response of the network to changes in training set size and meteorological conditions. In this paper, the authors present a procedure to set up the main characteristics of the network using a physical hybrid method (PHANN) to perform the day-ahead PV power forecast in view of the electricity market. The procedure outlined here can be adopted to set up the best settings of the network in terms of the number of layers, neurons, and trials.