# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 2 Machine Learning, Deep Learning and Related Methods
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Reference** & **Notes** \\ \hline
2018 & [21] & A review of ML and statistical models based on historical data. Concludes that ANNs and Support-Vector Machines (SVMs) are the best-performing models, especially due to their capability to rapidly adapt to varying environmental conditions. Genetic Algorithms (GAs) result as the most frequently used method in optimizing forecasting models’ hyper parameters. \\ \hline
2019 & [22] & A very interesting review, from the taxonomy point of view, of AI-based methods in solar power forecasting. Methods analyzed include ANNs, SVMs, Extreme Learning Machines (ELMs), Recurrent Neural Networks (RNNs), Long short-term memory (LSTM), RF, stacked Auto-Encoders, Generative Adversarial Networks (GANs), Fuzzy Logic (FL), Particle Swarm Optimization (PSO) and others. For each method is indicated their pros \& cons and optimal field of application. This paper outlines challenges and future research directions, mainly: probabilistic prediction of solar energy, model explainability and prediction of the movement and thickness of clouds. \\ \hline
2019 & [23] & A review focused only on DL methods for renewable energy forecasting, both deterministic and probabilistic (deep belief network, stack auto-encoder, deep recurrent neural network, etc.) Forecasting horizon from 15 min ahead to 120 min ahead. Some notes on data preprocessing techniques \\ \hline
2020 & [24] & A comprehensive review of papers from 2008 till 2019 on ML, DL and hybrid models to forecast power production from PV. Interesting concluding remarks. Mainly focused on methods for point forecasting. \\ \hline
2020 & [25] & A comparison of state-of-the-art models to forecast PV power production focused on a horizon of 36 h in advance. Many models tested from simple linear regression (also Ridge, Lasso and Elastic Net), the DT and ensemble models, both bagging (RF) and boosting (extreme Gradient Boosting). Robust 10-Fold Cross-Validation procedure to test each model’s performance and grid search to find each model’s optimal hyperparameters. All models were tested on a single dataset (plant located in Asia). Weather forecast and observations were used as model input. XGBoosting performed best. \\ \hline
2020 & [26] & A review focused only on three DL methods; LSTM, RNN, Gated Recurrent Unit (GRU) and a hybrid Convolutional Neural Network + LSTM (CNN+LSTM) to forecast solar irradiance and PV power production. Generally, LSTM performs overall the best but if enough data is available CNN+LSTM is the preferred model to choose. This paper highlights the use of RMSE as the most useful metric, allowing easy comparison of results. \\ \hline
2020 & [27] & A review of various reinforcement learning methods, both classical (multi-agent RL, etc.) and deep (Deep Q-network, etc.) in sustainable energy and electric systems. It is a more generic review not focused on PV but with a paragraph on MPPT worth reading to a general overview of RL. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review papers in forecasting power PV production. Publication year considered: 2018–2021. The vast majority of methods employed within this field are several types of NN architectures, but while older papers reported the use of shallow architectures such as multilayer perceptron (MLP) or Radial Basis Function (RBF) networks, research that is more recent has turned its interest to more advanced DL methods, such as LSTM, CNNs or a combination of both. Concerning the metrics used to assess models' performance, the most frequent are Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). It is quite impossible to compare results from models applied to different scenarios, where the scenario has to be intended as the features of the plant under investigation (dimension, architecture in terms of the number of string, cells type, etc.