# 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** & **Time Horizon and Resolution** & **Parameters Used** & **Point or Regional** & **Region and PV** & **Accuracy** \\ \hline \multirow{3}{*}{[41]} & \multirow{3}{*}{2010} & \multirow{3}{*}{ANN} & 24 h ahead & Meteorological parameters: & \multirow{3}{*}{1 point} & \multirow{3}{*}{Trieke, Italy} & \multirow{3}{*}{MAE \(<\) 5\%} \\  & & & 5 min & & air temperature & & 120 kW \\ \hline \multirow{3}{*}{[42]} & \multirow{3}{*}{2011} & \multirow{3}{*}{ANN} & 24 h ahead & Historical powers and & \multirow{3}{*}{1 point} & Huazhong, China & MAPE: 829\%, sunny day \\  & & & 5 min & meteorological forecast & & 28 kW & MAPE: 844\%,- rainy day \\ \hline \multirow{3}{*}{[40]} & \multirow{3}{*}{2012} & \multirow{3}{*}{SVM} & 1-day ahead & Historical powers and weather & \multirow{3}{*}{1 point} & China & \multirow{3}{*}{MSE = 8.64\%} \\  & & & 15 min & forecasts & & 20 kW & \\ \hline \multirow{3}{*}{[44]} & \multirow{3}{*}{2012} & \multirow{3}{*}{ANN} & \multirow{3}{*}{Up to 39 h ahead} & Historical powers and weather & \multirow{3}{*}{3 points} & La Riga, Spain & \multirow{3}{*}{MAPE = 0.85\%} \\  & & & & forecasts & & 36 kW & \\ \hline \multirow{3}{*}{[45]} & \multirow{3}{*}{2012} & \multirow{3}{*}{ANN} & 5 min-40 min & Historical powers & \multirow{3}{*}{1 point} & \multirow{3}{*}{Ratankol, Turkey} & \multirow{3}{*}{RMSE = 65 W} \\  & & & 1 min & & 700 Wp & & \\ \hline \multirow{3}{*}{[46]} & \multirow{3}{*}{2012} & \multirow{3}{*}{SVR} & 1 h ahead & \multicolumn{3}{c}{Forcasted parameters:} & \multirow{3}{*}{1 point} & \multirow{3}{*}{Kalayburn, Japan} & \multirow{3}{*}{MAE = 0.058 MWh} \\  & & & & & & 1 MWp & \\ \hline \multirow{3}{*}{[47]} & \multirow{3}{*}{2013} & \multirow{3}{*}{SVM} & 1 h ahead & Meteorological parameters: sky & \multirow{3}{*}{3 points} & Denver, Seattle and Miami, U.S. & \multirow{3}{*}{MAE : 35 W} \\  & & & & & & wind speed & \\ \hline \multirow{3}{*}{[40]} & \multirow{3}{*}{2014} & \multirow{3}{*}{ANN} & 24 h ahead & Historical powers. On-site solar & \multirow{3}{*}{1 point} & Papila, Italy & \multirow{3}{*}{MAPE: 2\%–12\%} \\  & & & 5 min & irradiance and cell temperature & & 1 MWp & \\ \hline \multirow{3}{*}{[48]} & \multirow{3}{*}{2014} & \multirow{3}{*}{ANN} & 24 h ahead & \multirow{3}{*}{min} & On-site measurements of air & \multirow{3}{*}{1 point} & Salerno, Italy & \multirow{3}{*}{MAE = 19.49\%} \\  & & & & temperature, and in plane solar & & & 960 kW & \\ \hline \multirow{3}{*}{[49]} & \multirow{3}{*}{2014} & \multirow{3}{*}{ANN} & 24 h ahead & On-site measurements of solar & \multirow{3}{*}{1 point} & Jean, Spain & \multirow{3}{*}{R2 = 0.98} \\  & & & & irradiance and air temperature & & 4 kW & \\ \hline \multirow{3}{*}{[50]} & \multirow{3}{*}{2015} & \multirow{3}{*}{ANN} & 1 day ahead & Historical powers, & \multirow{3}{*}{Regional forecast} & \multirow{3}{*}{Mingin, Garous} & \multirow{3}{*}{MAPE = 7.65\%} \\  & & & & temperature, aerosol indexes, wind speeds, and humidities & & 10 MWp & \\ \hline \multirow{3}{*}{[51]} & \multirow{3}{*}{2015} & \multirow{3}{*}{ENS} & 1 day ahead & Forecast of solar irradiance & Regional forecast & Italy & \multirow{3}{*}{nMAE : 127-4.04} \\  & & & 1 h & & & & \\ \hline \multirow{3}{*}{[52]} & \multirow{3}{*}{2015} & \multirow{3}{*}{k-NN} & \multirow{3}{*}{1 day ahead} & On-site measurements: solar & \multirow{3}{*}{3 points} & Sanflops, Broadway & \multirow{3}{*}{mMAE : 7.4, 6.38 and 7.74} \\  & & & & irradiance, temperature, wind speed, and relative humidity & & & 492 kW, 5.21 kW, & \\ \hline \multirow{3}{*}{[53]} & \multirow{3}{*}{2016} & \multirow{3}{*}{ANN} & 1 day ahead & \multirow{3}{*}{The use measurements:} & \multirow{3}{*}{\multirow{3}{*}{1 point}} & \multirow{3}{*}{Trail Nada, India} & \multirow{3}{*}{MAPE : 1.92\%–11.28\%} \\  & & & & temperature, wind speed, and relative humidity & & & 20 kW \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Machine learning (ML)-based methods for the forecast of PV power-period 2010-2019.