# Advanced Methods for Photovoltaic Output Power Forecasting: A Review
## 3 AI-Based Techniques Used for PV Output Power Forecasting
### Application of Machine Learning in PV Power Forecasting
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref and Authors. ** & **Year** & **Method** & \begin{tabular}{c} **Time Horizon and** \\ **Resolution** \\ \end{tabular}  & \begin{tabular}{c} **Parameters Used** \\ **Forecast** \\ \end{tabular}  & \ \begin{tabular}{c} **Point or Regional** \\ **Forecast** \\ \end{tabular}  & \begin{tabular}{c} **Region and FV** \\ **Nominal Power** \\ \end{tabular}  & **Accuracy** \\ \hline
[54] & 2016 & SVR & \begin{tabular}{c} 12 h ahead \\ min \\ \end{tabular}  & \ \begin{tabular}{c} On site measurements solar \\ irradiance and module \\ temperature \\ \end{tabular}  & \(1\,\mathrm{point}\) & \begin{tabular}{c} Melaka, Malaysia \\ 6 iWP \\ \end{tabular}  & RMSE: 42\%-6.8\% \\ \hline
[55] & 2016 & MME & 1 day ahead & NWP models & \(1\,\mathrm{point}\) &
\begin{tabular}{c} \(\,\mathrm{[662\,\mathrm{h}\mathrm  
In [47] the authors described a SVM-based model for the short-term forecasting of the power produced by three PV plants installed in three different regions of the U.S. The data used to develop the model was provided by the National Solar Radiation Database (NSRDB). This study showed that normalizing the solar irradiation values with respect to the transmissivity gives better results than using a standard sigmoid function-based normalization. The proposed technique was compared with a RBF-NN-based model showing a better accuracy. The authors suggested that the addition of the information regarding the sky cover can improve the prediction accuracy. A simple but accurate approach for short-term forecasting regarding a large-scale PV plant is presented in [40]. The model was calibrated using the on-site measured powers, cell-temperatures, and solar radiations regarding a one MWp PV plant installed in the southern part of Italy. This work showed that adding more hidden layers does not lead to better accuracies, but increases the convergence time. The authors pointed out that the proposed models do not require any additional parameter and that they can be easily implemented. A multiple regression analysis has been performed in order to determine the most significant input parameters (among module and air temperature, in-plane solar irradiation, and produced power) to be used in an extreme machine learning NN used to forecast the power produced by a 960 kWp PV system located at Salerno, Italy [48]. The model was evaluated for a forecasting horizon in the range [1-24 h]. In [49] the authors developed a dynamic NN to forecast one hour-ahead the power produced by a 44 k Wp PV plant installed at the Jean University in Spain. In this case, an ANN was used to predict the solar irradiance and the air temperature that are then used as an input of a second ANN forecasting the produced power. In [50] the authors developed a method using an ANN for a one day-ahead PV power forecasting. The aerosol index has been used as an input as the solar irradiance is a parameter that is not always measured and/or available. The MAE was 7.65%, and the authors concluded that in the future data from remote sensors could represent a valuable input in the field of PV power forecasting. Different methods have been investigated in [51] including a grey-box model, NNs, k-nearest neighbors (kNNs), quantile random forest (QRF), SVM, and ensemble of methods (ENS). The application of these techniques gave similar performances showing a MAE close to 5%. However, ENS was the best forecaster considering variable weather condition. The authors concluded that the investigated methods proved the feasibility to produce good results even without using the temperature as an input parameter. In [52] the authors used a k-NN-based method for forecasting the power produced by small-scale PV plants installed in three different regions: SanDiego, Braedstrup and Catania. They concluded that simple techniques such as _k_-NNs can produce relatively accurate forecasts (the nMAE was in fact 0.96%).