# Advanced Methods for Photovoltaic Output Power Forecasting: A Review
## 1 Introduction
\begin{table}
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**Ref** & **Year** & **Authors** & \\ \hline
[23] & 2008 & [PERSON] and [PERSON] & This review is on a number of the first footcaster’s techniques using a NANS for the forecasting of the power produced by PV systems. It was shown that AI based techniques have a great potential in the estimation, and recurrent neural networks-based (BNNs) were recognized as the most accurate forecasts. Also, long-short term memory (LSTM) had been widely used. \\ \hline
[4] & 2016 & [PERSON] et al. & This paper includes regression techniques and AI-based methods. Authors found out that statistical approaches perform better than parametric approaches. They also mentioned that most recent techniques are based on the use of ML methods (including SVM, ELM, ESIV, FL, etc.), due to the changes of modeling without the need to know the PV plant characteristics. \\ \hline
[5] & 2016 & [PERSON] et al. & The authors provided a complete review including these, ANNs, and some hybrid approaches. A comparison between ANN-based and classical line series models was also presented. The conclusions were that the forecast accuracy can be enhanced by pre and post-processing the historical data. ANNs gave better performances than other classical these approaches. \\ \hline
[26] & 2017 & [PERSON] et al. & The authors wrote that the methods based on the forecasting of solar irradiance and cell temperature are the best forecasting approaches when there are rapid fluctuations in the PV power (especially occurring with a sky partially clouded). They also observed that a combination of satellite and land-based sky imaging improve the forecasting results in the case of very short forecasting. \\ \hline
[27] & 2017 & [PERSON] et al. & The authors presented an extended comparative study between physical and hybrid methods. The conclusion was that the physically-brid-artificial neural networks (PIAN) always show the highest accuracy. \\ \hline
[28] & 2018 & [PERSON] et al. & In this work, a general review on recent studies on the direct forecasting methods was conducted. The authors pointed out that ANN and SVM-based forecasting models perform particularly well under rapid and varying environmental conditions. Optimized algorithm can significantly increase the forecasting accuracy, and genetic algorithms (GA) represent one of the most practical optimization techniques for the forecasting of PV power. \\ \hline
[29] & 2018 & [PERSON] et al. & The main conclusion of this review was that ANN and SVM-based methods are widely used due to their ability in solving complex and non-linear forecasting problems. Ensemble methods were found capable to improve the forecasting accuracy as they’are able to merge linear and non-linear methods. \\ \hline
[30] & 2019 & [PERSON] et al. & A review on ML and metaheuristics methods for solar radiation and PV power forecasting has been presented in this work. The authors reported that hybrid models based on ML and metaheuristic methods could contribute to improve the forecasting accuracy, while GA-based techniques represent the most viable optimization method [28]. \\ \hline \end{tabular}
\end{table} 
Table 1: Review papers on photovoltaic (PV) power forecasting. The paper is organized as follows: a brief introduction about artificial intelligence techniques, including machine learning and deep leaning is provided in Section 2; Section 3 presents the recent applications of AI techniques in PV power forecasting; concluding remarks and future directions will be provided in the last section.