# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 2 Machine Learning, Deep Learning and Related Methods
WPD is applied to a PV original power series obtainingfour \"sub-series\"; each new derived series, augmented with the meteorological data, constitutes the input of an LSTM whose results are linearly weighted to provide the final forecast. Each LSTM provides a multi-step prediction. An LSTM network is also used in [18] where its inputs include historical PV power data, historical weather predictions and synthetic weather forecasts derived using the k-means clustering method to provide multi-step-ahead forecasts. The derived synthetic irradiance forecast results in an improvement into models' accuracy that varies from 33%, if compared to that when an hourly categorical type of sky forecast is used, to 44.6%, if compared to that when a daily type of sky forecast is used. This work claimed that the proposed LSTM DNN can perform better than the recurrent neural network (RNN), the generalized regression neural network (GRNN) and the ELM models. Again, a model with an LSTM DNN in conjunction with an RNN is applied to forecast PV production in [37]. This work introduces a time correlation modification (TCM) integrated with a partial daily pattern prediction (PDPP) framework. The main idea is that the ensemble resulting from LSTM-RNN+TCM can benefit both from the results of the time correlation model, which is closer to the actual data in trend, and from the results of the LSTM-RNN model more capable of tracking the fluctuations of PV power output. Finally, the DPP model is used to predict the pattern of the forecasting day so as to select an optimal set of weight coefficients to calculate the results using the output from both the LSTM-RNN model and TCM model. As the authors claimed, the methodology of Transfer Learning (TF) firstly appears in a research paper in the field of PV production forecasting in [38]. Transfer learning is a known technique employed in DNNs that consists of using a complex but successful pre-trained DNN model to \"transfer\" what it has learned from its specific domain knowledge to a similar but different domain. Transfer learning has been extensively adopted in the field of image classification/recognition for convolutional neural networks (CNNs). The advantages coming from TL related to the existing successful pre-trained model consists in:  
* Its hyper-parameters and network structure, i.e., number of layers and types, have already been tested and found to be successful;
* The earlier layers of a CNN are essentially learning the basic features of the image sets such as edges, shapes, textures, etc. Only the last one or two layers of a CNN are performing the most complex tasks of summarizing the vectorized image data into the classification. Weights of the first layers are frozen while only the last layers are trained for the specific task in the target domain knowledge; this turns out to be a faster training method. This idea in the field of PV power forecasting relies on transferring the knowledge of a pre-trained LSTM in the field of a historical irradiance time series to that of PV power series (irradiance being highly correlated to PV power) to cope with data scarcity in the target domain. Authors have obtained interesting results that demonstrate how TL can be very beneficial for a new plant where there is not enough historical data acquired. To provide short-term predictions of PV power output, authors in [39] propose the use of an ensemble method, LighGBM, combined both with a Bayesian optimization algorithm to find optimal time steps for temporal pattern aggregation and a clustering-based training framework based on a tree-structured self-organized map (TS-SOM), proving its effectiveness in a production environment consisting of an edge computing platform (Raspberry Pi 3B) with limited storage. The proposed model, starting from historical meteorological data, applies three functional steps: a temporal pattern aggregation optimized using a Bayesian approach, a weather clustering, performed by TS-SOM, and the final model training using LightGBM.