# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 2 Machine Learning, Deep Learning and Related Methods
In this paper the short-term forecasting horizon 1-6 h is explored; the main novelty resides in the considered PV plant size, a large multi-megawatt PV system (a 75 MW plat with 84 inverters), for which a new approach consisting of macro-level models results into a marginal improvement in accuracy compared to the usual inverter-level model approach. The proposed model uses an FFNN, an LSTM-RNN and a gated recurrent unit-RNN (GRU-RNN). The same CI criteria are used to provide a probabilistic description of the accuracy provided by a Gaussian Process Regression with Matern 5/2 as kernel function in [50]. As commonly employed in the forecasting PV output field, the proposed model uses meteorological data (irradiance, temperature, and zenith and azimuth solar positions) and historical PV output as inputs. A k-means algorithm is used to cluster data into four groups based on solar output and time. The proposed model is validated using five PVplants data and both a five-fold CV procedure and a hold-out one (using 30 random days as a test). A first work more oriented on the probabilistic forecasting of PV production that summaries the models' accuracy in terms of the PI is [51]. Here, in the addition to the usual point forecasting metrics such as RMSE and MAE, prediction interval coverage probability and prediction interval normalized average width (PINAW) are introduced; the first metric estimates the predicted reliability, which is based on the probability that the real PV power is within the PIs, while the latter measures the width of the PIs. In this paper is proposed an hourly day-ahead forecasting horizon and sampling, and is introduced a CNN combined with a quantile regression (QR) method with a two-stage training strategy to cope with the non-differentiable loss function of QR. Results obtained with the described model are very interesting also in comparison to that obtained by a quantile extreme learning model (QELM), quantile echo state network (QESN), direct quantile regression (DQR) and RBFNNs. Another researcher paper considering probabilistic forecasting is [52]; here, the authors use a hybrid model made up of a wavelet transform (WT) applied to historical PV power data and a RBFNN that is trained using a PSO algorithm. The proposed hybrid model provides the point forecast while constructing a PI is employed an indirect method: bootstrap. Results in PI using bootstrap are compared, using reliability diagrams, to direct and indirect QR; from this comparison, bootstrap emerges as a paramount factor in determining the better performing model. An Analog Ensemble (AnEn) model is used in [53]; the authors, starting from the AnEn developed in [54], have further improved the metric herein adopted to allow the management of data, both from NWP and from satellite images (used to derive GHI time series data), where the probability density function (PDF) of the analogue ensemble is built up using a weighted kernel density estimation (KDE) method. Results are compared with a quantile regression forest (QRF) and a Bayesian Regression (BR) with Automatic Relevance Determination (ARD) prior models. Forecasting results are described in terms of PINAW and Continuous Ranked Probability Score (CPRS) and show how the proposed model performs better, compared to QRF and BR, for a forecasting horizon of fewer than two hours, while above this threshold QRF seems to perform better. The dataset used in the 2014 Global Energy forecast competition (GEFCom2014) is used in [55] to test a novel method able to provide a probabilistic forecast. The proposed method, named nearest neighbours quantile filter (NNQF), solves the problem of training quantile regressions with gradient-based optimization by deriving a modified training set. This modified training set can be used to train a generic regression model that directly outputs the conditional empirical q-quantile defined by the neighbours used in the training.