# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 2 Machine Learning, Deep Learning and Related Methods
When compared with common DL alternatives such as GRNN and LSTM, authors showed that the proposed method performs better with a dramatic decrease of both training time and inference time. A hybrid model made up of a set of different ML-based methods is described in [40] to forecast PV power production in the short-term horizon. In the first step, an RF model is used to rank the input, weather-related (such as temperature, daily rainfall, horizontal radiation, diffuse horizontal radiation, etc.) features, then an improved grey ideal value approximation (IGIVA) model receiving results from RF as weight values searches for similar days of different weather types to improve the training data. Then, the original power series is decomposed by a complementary ensemble empirical mode decomposition (CEEMD) algorithm, while, to provide the short-term PV power generation, a backpropagation NN (BPNN) trained using a dynamic factor PSO method (DIFPSO) is used. Again, the short-term horizon is investigated in [41] using an ensemble model made up of two LSTMs with Attention Mechanism (AM) working on the temperature and power time series, respectively, whose results are flattened and merged by a fully connected layer. The AM in DL is based on the concept of directing a model's main focus by paying greater attention to certain factors when processing the data. In broader terms, attention is one component of a network's architecture and is in charge of managing and quantifying the interdependence between the input and output elements (General Attention) or within the input elements (Self-Attention). Authors proved that AM can effectively improve LSTM performance. The public dataset of the GEFCom14 competition is used to forecast PV generation for one day ahead with data sampled hourly in [42]. Here, an ensemble method with cluster analysis is proposed. A k-means algorithm is used to cluster solar generation, and the result of each cluster is used in an ensemble, by ridge regression, of RF models. Every ML-based method, being data-driven, needs an adequate amount of data; this means that, before being able to provide a forecast, it is necessary to acquire data for a non-negligible amount of time, ideally at least one year to take into account annual seasonality. In this regard, methods such as generative adversarial networks (GANs) could be useful to derive enough data for training an ML-based method. In [43] a recurrent generative adversarial network (R-GAN) is used to generate realistic energy consumption data by learning from real data. Although not strictly pertinent, this work has been included, as, in the authors' opinion, such an approach could be effectively used in the forecasting in the field of PV production, for, as an example, generating weather or power data for the rainy or cloudy conditions that are usually the conditions resulting in lower accuracy predictions. While papers listed so far are related to what is known as \"point-forecast\", a far fewer number of papers have been published during the last several years concerning probabilistic forecasting. In this regard, some international forecasting contests, for example, M3 and M4 forecasting competitions, have contributed to encouraging the production of such types of forecasting results. These contests have highlighted some concepts, such as prediction interval (PI) and probability coverage, and some metrics more suitable for this type of forecast, such as pinball loss. For more information concerning this contest see [44; 45; 46]. In [47], the authors have provided a point-forecast with a confidence interval (CI) which quantifies the uncertainties associated with the forecasts delivered by mean of a bandwidth of possible changes and the certainty associated with each forecast. In this research, the authors employ a bootstrapping method to compute the CI. It is here interesting to highlight that confidence interval (CI) and prediction interval (PI) are completely different concepts, with the first being far narrower than the second (see [48; 49]).