# Advanced Methods for Photovoltaic Output Power Forecasting: A Review
## 3 AI-Based Techniques Used for PV Output Power Forecasting
### Hybrid Methods-Based Forecasting  
Table 4 summarizes the main research regarding hybrid methods carried out during the period 2012-2019. A number of forecasting techniques using no exogenous inputs for the prediction of the power produced by a 1 MWp power plant have been presented in [70]. The hybrid genetic algorithm ANN-based outperforms the other investigated techniques such as kNN, ANN, and ARIMA-based. In [25], the authors introduced a hybrid model for the short-term power forecasting of a small-scale PV system installed on the rooftop of the municipality of Trieste, Italy. The model combines a SVM approach with a seasonal auto-regressive integrated moving average method (SARIMA). The result was that the designed hybrid model performs better than the SVM and SARIMA model working alone. In order to improve the speed of convergence and the accuracy of the prediction, a new hybrid genetical swarm optimization (GSO)/error back propagation (EBP)-based technique was developed in [71]. The method was tested for the 24-h power forecasting regarding a small-scale plant installed at Milano, Italy. The result was an improvement of performances with respect to the standard EBP alone. A hybrid methodology combining SVM, self-organization map (SOM), and fuzzy logic (FL) for a one day-ahead forecasting is proposed in [72]. In this work, SOM was used to classify the type of weather, the SVM for the training phase, and the FL in order to select the most accurate training model coming from the SVM. The data used to develop the method was from the Taiwan Central Weather Bureau (TCWB). This hybrid forecaster outperformed both the traditional SVR and ANN-based models. In [73], the authors presented an optimized GANN-based method for intra-hourly reforecasting the power produced by a large-scale plant installed in San Diego, U.S. The results showed the effectiveness of the optimized reforecasting method in reducing the errors produced by a number of different forecast methodologies including deterministic models, k-NN, and ARMA. In [74] the authors developed a method for forecasting hour-ahead the power produced by a 5 kWp PV plant installed in Taiwan. The hybrid method consists of a fuzzy k-means algorithm for the classification of the historical daily PV power, five RBFN to forecast the produced power, and a fuzzy logic controller to select the appropriate RBFN for the considered conditions. In this case, the input data were the actual solar irradiation, precipitation level, and the temperature forecast. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref and Authors** & **Year** & **Method** & **Time Horizon** & **Parameters Used** & \ \begin{tabular}{c} **Point Regional** \\ **Forecast** \\ \end{tabular}  & \begin{tabular}{c} **Region and PV** \\ **Nominal Power** \\ \end{tabular}  & **Accuracy** \\ \hline
[70] & 2012 & ANN-GA & 1 h and 2 h ahead & \ \begin{tabular}{c} Historical measurement of the \\ output power \\ \end{tabular}  & 1-point & \begin{tabular}{c} Central California, US \\ 1 MWp \\ 2 h-MAE = 9.53 kW \\ \end{tabular}  \\ \hline
[23] & 2013 & SVM-ARIMA & \begin{tabular}{c} 1 h ahead \\ 5 min \\ \end{tabular}  & \begin{tabular}{c} Historical measurement of \\ sources \\ \end{tabular}  & 1-point & \ \begin{tabular}{c} Trieste, Italy \\ 20 kW \\ \end{tabular}  & \begin{tabular}{c} MF = 2.73\% \\ \end{tabular}  \\ \hline
[71] & 2013 & MLP-GSO-Physical & \ \begin{tabular}{c} Weather forecasts and historical \\ power \\ \end{tabular}  & 1-point & \begin{tabular}{c} Milano, Italy \\ 30 kW \\ \end{tabular}  & MAE = 0.317 kW \\ \hline
[72] & 2013 & SVM-FL-SOM-LVQ & 1-day ahead & \ \begin{tabular}{c} Air temperature, probability of \\ precipitation, and solar irradiance \\ \end{tabular}  & 1-point & \begin{tabular}{c} Taiwan \\ 5 kW \\ \end{tabular}  & MAE = 1.79-4.69 \\ \hline
[73] & 2015 & ANN-GA, hNN & Intra-hour & \ \begin{tabular}{c} Historical powers and imaging \\ data \\ \end{tabular}  & 1 point & \begin{tabular}{c} Sandage, US \\ 48 MWp \\ \end{tabular}