# Advanced Methods for Photovoltaic Output Power Forecasting: A Review
## 4 Concluding Remarks and Future Trends  
In this paper, an extensive review of the recent applications of AI techniques-including ML, DL, and hybrid methods-to PV output power forecasting, is presented. The key conclusions and future directions that can be inferred are:  
While the development of forecasters based on ML in general has been investigated rather intensively, the application of DL for PV power prediction has been rather limited so far. Most researchers have focused on forecasting at single locations, while little work has been done on regional models; no accurate general regional model has been proposed to date. The most investigated time horizon is in the short-term regime (up to few days)--which is also the most requested and used. ML-based forecasters are well suited for this case, particularly when combined with appropriate algorithms--such as ANN-optimized GA or PSO. Very-short-term forecasting and long-term forecasting have been scarcely investigated. Most AI-based models perform well for sunny days, while for cloudy days the forecasting accuracy decreases significantly. The accuracy of AI-models decreases for longer time horizons, especially beyond 72 h.  
ML methods based on historical power output, and the use of meteorological parameters (such as air temperature, solar irradiance, relative humidity, wind speed, cloud cover), combined with an optimal learning algorithm and weather classification can improve forecasting accuracy. One-step ahead forecasting performs best, and has been extensively investigated. Conversely, multistep-ahead predictions remain a challenging task. Hybrid models (e.g., the combination of physical models with ML methods such as ANN) improve forecasting accuracy. Generally, in order to increase the accuracy of the forecasters based on AI techniques, the following points should be considered: (1) large datasets with good-quality data are preferable; (2) pretreatment and analysis of the database to identify outliers and missing data is required; (3) exogenous inputs should be taken into account, such as for example cloud variation; (4) combination with other physical models. Long-term PV power forecasting could be achieved by using NWP models and DL including LSTM and CNN, as the latter (as well as RNN) have some useful characteristics, such as the ability of estimating the temporal dependencies of the investigated data, and the ability of performing a more general feature extraction. With the increasing amount of data (on-site measurements based on SCADA, satellite image data, NWP models), DL will become increasingly interesting for PV power prediction, in particular for designing accurate regional forecasters. An extensive investigation is still needed on DL-based forecasting methods. DL has been gaining popularity in particular for time series forecasting, due to the availability of increasingly large datasets and open sources codes (e.g., Python, TensorFlow, Keras, etc.). ML based models, on the other hand, have shown their capability for PV power forecasting as well as DL based models, while the accuracy depends mainly on the data quality, their amount and the learning algorithms. However, DL based models are more suitable in the case of large database, unlike ML based models. The improvement of model accuracy for cloudy days is still only marginally investigated. Forecasting approaches able to estimate and classify cloud cover and to use these parameters for DL models is expected to lead to sizeable accuracy improvement. The combination of physical models with DL-based methods (e.g., DCNN) has been scarcely investigated. However, such an approach could significantly improve forecasting accuracy. Conceptualization, A.M.; methodology, A.M. and A.M.P. ; validation, A.M.P. and E.O. ; formal analysis, S.L. ; investigation, A.M. and S.L. ; resources, A.M.; data curation, A.M.P. and E.O. ; writing--original draft preparation, A.M.; writing--review and editing, A.M.P., E.O., and V.L. ; visualization, A.M. and A.M.P. ;supervision, S.L. ; project administration, S.L. and A.M.P.