# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 2 Machine Learning, Deep Learning and Related Methods
), the environmental conditions, length of the training and test dataset, features pre-processing and/or features engineering, if applied, as the scenario greatly affects the model's performance results. This is true for metrics such as RMSE or MAE but also for percentage metrics such as Mean Average Percentage Error (MAPE) or Root Mean Squared Percentage Error (RMSPE) that are more suited to comparing models' performances related to different plants, being the last percentage errors. For a detailed discussion on models' metrics see [28; 29]. While many researchers are more interested in providing a model capable of providing \"point-forecast\" results, i.e., the expected mean/average value for the forecasting horizon, some papers have been published concerning the \"probabilistic/interval forecast\", where, in addition to the point-forecast, the prediction interval associated to this point is provided [30]. For the reasons outlined before, PV power forecasting can be classified into basically two different types:  
* Point-forecast
* Interval-forecast  
The latter type, in the authors' opinion, could be more useful as in many situations it may be more critical to know not the future power production from the PV plant connected to the grid but rather to know, with a probability of 95% or 99%, that this expected level of production will not fall below a critical level. The following tables, Tables 2 and 3, summarize the latest research in forecasting PV production by grouping the research into point and interval forecasts. As appears evident from Tables 2 and 3, the vast majority of papers are point-forecast and consider a short-term forecasting horizon. In this regard, the usual classification of models based on the forecasting horizon is the following:  
* Very short-term, from few seconds to some minutes;
* Short-term, up to 48 or 73 h;
* Medium-term, in the range from few days to one week;
* Long-term, usually several months or one year. Another, mostly equivalent, classification criteria relies on counting how many time steps ahead are considered in the forecasting horizon. Many papers are focused on only one step ahead (this usually could be a single hour or day), but multi-steps-ahead models are often the more interesting ones. A multi-step ahead model can produce results into an iterative model or with architectures able to provide, with a single run, or better a single inference computation, an array of values, each related to a specific timestamp (Pt + 1, Pt + 2,... Pt + h, where P is the forecasted power production, h is the forecasting horizon and t is the actual timestamp). The remainder of this section is devoted to highlighting the novelty and/or the most interesting findings of each of the listed research papers. In [31] a multi-step-ahead prediction model focused on 1 to 16 steps ahead (with data sampled every 15 min and so resulting in a forecasting horizon from 15 min to 4 h) is obtained by a deep extreme learning machine (DELM) combined with enhanced colliding bodies optimization (ECBO) and Variational Mode Decomposition (VMD). The proposed model employs irradiance prediction from numerical weather prediction (NWP) and uses as the first step a grey correlation analysis coupled with Pearson correlation to find in the training data a day representative of/like the prediction day. In the second step, VMD and ECBO methodsare employed to decompose the original power data that is fed to a Deep Extreme LM (DELM) to provide the final forecasts. The proposed DELM can be trained very fast if compared to a generic DL model. The decomposition method employed in this work is a novelty as most previous works rely on wavelets packet transform (WPT) or empirical mode decomposition (EMD). The model has been tested on a PV plant in China using a dataset of two years (2018-2019) with data sampled every 15 min and differentiating according to day conditions: sunny, cloudy and rainy.