# Advanced Methods for Photovoltaic Output Power Forecasting: A Review
## 3 AI-Based Techniques Used for PV Output Power Forecasting
### Hybrid Methods-Based Forecasting
To enhance the accuracy in the forecasting, an ELM has been optimized using different adaptive PSO algorithms (APSO-ELM) in [77]. The result was a better performance in the short-term solar forecasting with respect to other techniques. A method combining a NN-based approach with an analog ensemble (AnEn) was proposed in [78] for the short-term forecasting of the power produced by a number of PV arrays. The method was tested for three different PV plants installed in Italy with a time horizon of 72 h, and according to the authors the proposed solution is particularly suitable for massive scale computation. It has previously demonstrated in [24] that a dual layer ANN hybridized with the Clear sky solar radiation algorithm, namely the physical hybrid neural network model (PHANN), improved the day-ahead forecast accuracy compared to the pure ANN. In addition, it was demonstrated that the PHANN method always outperformed the day-ahead power forecast obtained with the physical five parameter equivalent model of a PV module, located in Milan, Italy [27]. Besides, a mixed forecasting technique is described in [79]. Here, the authors improved the accuracy of some weather forecasts using a physical neural network model (PHANN) and optimizing the five-parameter model with a social network optimization (SNO). The method clearly improved the forecast accuracy in the prediction of the power produced by a PV plant installed at Milano, Italy. The same hybrid method scored better results with different time horizon forecasts [80]. In [81], a hybrid technique using a variation mode decomposition (VMD) method together with a CNN was developed for the short-term power forecasting of a 100 kWp plant installed at Nanjing, China. The VDM was used to decompose the PV power time series into different frequencies and into 2D data, while the power was estimated by a SVR model. It has been shown that the method presents a higher prediction accuracy with respect to other 1D VMD-based forecasting methods. A hybrid WT-PSO-SVM- based method for the short-term prediction of the power produced by a PV system installed in China was presented in [82]. In this case, the input data were the PV system powers and the meteorological parameters from NWP. The results showed the better performance of the designed technique with respect to others based on HGNN, HPNN, BPNN, and SVM. In [83] a hybrid SVM-GA model has been devised and validated for the short-term PV power forecasting. The SVM technique was used to classify the weather, while the GA to optimize for the optimization of the model. The hybrid GA-SVM model outperformed the SVM in point of view accuracy forecasting. LSTM networks have been used for one day ahead power forecasting regarding a large-scale PV plant in China [84]. The method classifies the weather into ideal and non-ideal conditions, and a discrete grey model has been developed for the power prediction. A hybrid model based on stationarity wavelet networks (SWN), LSTM, and DNN for the short-term forecasting of the power produced by a small-scale and a large-scale PV plant has been presented in [85]. The model used the temperatures estimated by the SWM, while the LSTM was used to extract the historical powers, and the DNN to predict the powers.