# 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  
With reference to the period 2011-2019, a comprehensive summary of the most relevant researches dealing with the applications of ML-based methods in the field of PV power forecasting is reported in Table 2.  
An indirect method to estimate the power produced by a PV plant 24-h ahead has been described in [41]. The method uses as an input the forecasted solar irradiance and is based on a simple multi-layer perceptron neural network (MLP). The mean absolute error (MAE) is lower than 5% for all examined case studies. A conclusion of this work was that using more input parameters such as cloud cover, pressure, and wind speed could improve the model accuracy. The same happens when the quality and the size of the database are improved. A RBF-NN for a 24-h ahead online PV power forecasting is shown in [42]. In this work, a self-organized map (SOM) is utilized in order to classify the input variables. The developed model accepts as an input the mean daily values of solar irradiance, temperature, wind speed, and historical powers. The performance of this model is represented by a MAPE of 8.3% corresponding to sunny days, and a 54.4% corresponding to rainy days. The model was tested for a small-scale 28 kWp PV plant installed at Huazhong, China. In [43] the authors described a method for a one-day ahead PV power output forecasting. The method based on SVM technique works with a weather classification, accepts as an input the historical dataset of power together with the weather forecasts for the next day. Four models have been developed corresponding to four typical days: cloudy, foggy, rainy, and sunny. The average MRE was 8.64%, while the best results have been achieved for sunny days (in this case the MAE was 4.85%). A short-term ANN-based forecaster is described in [44]. In this case, the input data were the historical electrical energy generation and the weather forecasts coming from a NWP model. The MLP-NWP performed better than other investigated ARIMA and K-NN, while ANFIS, MLP-NWP based-models outperformed all the tested forecasters. Considering different forecasting horizons from 16-39 h, the average root mean squared error (RMSE) was 11.79%, while the average MAE was 6.41%. In [45] the authors developed a simple NN for the short medium-term forecasting of the power produced by a PV small-scale plant installed in Istanbul, Turkey. In this case, only the historical powers have been used to design and verify the model that works well only for a time horizon in the range [5-40 min]. A support vector regression (SVR)-based method is used for the power of a 1 MWp plant installed at Kitakyushu, Japan [46]. The model accepts as an input different the cloudiness level, the relative humidity, and the extraterrestrial insolation. The obtained results are represented by an RMSE corresponding to 0.0948 MWh, and the model has shown bad performances in the case of partially clouded days. The authors claimed that there is no need to use relative humidity and air temperature as an input of the model.