# Advanced Methods for Photovoltaic Output Power Forecasting: A Review
## 3 AI-Based Techniques Used for PV Output Power Forecasting
### Hybrid Methods-Based Forecasting
 & MAE = 20.7 kW \\ \hline
[24] & 2015 & PHANN & Up to 72 h & Weather forecast and & 1-point & \ \begin{tabular}{c} Milano, Italy \\ 264 MWp \\ \end{tabular}  & \begin{tabular}{c} NMAE = [6.4\%-12.5\%] \\ \end{tabular}  \\ \hline
[74] & 2015 & PHANN & \begin{tabular}{c} Urban \\ measurements \\ \end{tabular}  & \begin{tabular}{c} 1 day ahead \\ and precipitation \\ \end{tabular}  & \begin{tabular}{c} Actual solar irradiance and \\ 25 kW \\ \end{tabular}  & \begin{tabular}{c} Actual solar irradiance and \\ 5 kW \\ \end{tabular}  & \ \begin{tabular}{c} Actual solar irradiance and \\ 5 kW \\ \end{tabular}  & \begin{tabular}{c} Actual solar irradiance and \\ 5 kW \\ \end{tabular}  \\ \hline
[75] & 2015 & Fuzzy-K means, & 1 day ahead & 1 day ahead & \ \begin{tabular}{c} Critical maximum temperature \\ and precipitation \\ \end{tabular}  & 1-point & \begin{tabular}{c} Taiwan, \\ 5 kW \\ \end{tabular}  & MAE = 3.25\% \\ \hline
[76] & 2017 & Hybrid & \begin{tabular}{c} Urban \\ WT-CDNN-QR \\ \end{tabular}  & \begin{tabular}{c} 15 min, 30 min, 1 h \\ and 2 h ahead \\ \end{tabular}  & \ \begin{tabular}{c} Historical measurement of the \\ output power \\ \end{tabular}  & 2 points & \begin{tabular}{c} Belgium, China \\ 1.5 MW \\ \end{tabular}  & MAE = [0.58\%, 296\%] \\ \hline
[77] & 2018 & PHANN & 1 day ahead & \ \begin{tabular}{c} Weather and historical \\ measurement \\ \end{tabular}  & 1-point & \begin{tabular}{c} Milano, Italy \\ 245 MWp \\ \end{tabular}  & NMAE = 5.1\% \\ \hline
[78] & 2018 & Hybrid & \begin{tabular}{c} 15 min, 30 min 6 min \\ ahead \\ \end{tabular}  & \ \begin{tabular}{c} Historical data solar irradiance \\ and temperature \\ \end{tabular}  & 1-point &
\begin{tabular}{c} Bhub \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref and Authors** & **Year** & **Method** & **Time Horizon** & **Parameters Used** & **Point Regional** & **Region and PV** & **Accuracy** \\ \hline
[80] & & & & & & & & \\ [PERSON] et al. & 2018 & PHANN & 1 day ahead & Weather forecasts, day of the year, and location & 1-point & Milano, Italy & NMAE = 3.39\% \\ \hline
[81] & & Hybrid & Various hour time & Historical power & 1-point & Nanjing, China. & \\ [PERSON] et al. & 2019 & VMD-CNN-SVR & scales & Historical power & 100 kWp & \multirow{2}{*}{MAE = 1.5\%} \\ \hline
[82] & & Hybrid & 1 day ahead & SCAD historical powers, & 1-point & Beijing, China. & \\ [PERSON] et al. & & WI-PSO-SVM & NWP meteorological & 1-point & 480 kWp & NMAE = 0.4\% \\ \hline
[80] & & & & & & \\ [PERSON] et al. & 2019 & Hybrid GA-SVM & 1 h ahead & On-site measurements of PV & \multirow{2}{*}{1 point} & Deakin, Malaysia & \multirow{2}{*}{MAFE = 98.76 W} \\  & & & & & & \\ \hline
[84] & & & & & & \\ [PERSON] et al. & 2019 & LSTM-NN & 2 days ahead & On-site weather data and & \multirow{2}{*}{1 point} & Beijing, China & \multirow{2}{*}{MAD = 1.41\% and 397\%} \\ \hline
[85] & & & & & & 10 kWp & \\ [PERSON] et al. & 2019 & SWT-LSTM-DNN & 24 h ahead & Historical PV powers and & \multirow{2}{*}{1 point} & Florida, USA, & \multirow{2}{*}{12.6 MW} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._A model based on a hybrid deterministic-probabilistic method for PV power forecasting has been described in [75]. The technique combines WT to decompose the signal, deep convolutional neural network (DCNN) to extract the nonlinear features and invariant structures in the decomposed signal, and quantile regression (QR) to statistically evaluate the forecasted power. The model was evaluated at two different locations and for different time horizons (14 min, 30 min, 1 h, and 2 h). The model was trained only using historical output powers, and compared with other approaches based on SVM, BPNN, and SVM-WT. The effect of different training approaches for the day-ahead forecasting of the power produced by a 245 Wp PV plant installed in Milano, Italy, was analyzed and discussed in [76]. In particular, the influence of the dataset size and the way in which data were employed in the training step was found to be of paramount importance.