# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 8 Concluding Remarks and Future Trends  
In this paper, a literature review of recent (from the year 2018 till 2021) applications of ML methods on many different fields of PV has been carried out. Fields touched within this discussion are forecasting of PV production, anomalies detection and fault analysis, tracking MPP, PV systems efficiency optimization, PV/T and CPV system design parameters optimization and efficiency improvement and energy management of PV storage systems. In almost all fields reported above, ML methods have proven to be an effective and reliable solution. The field of forecasting PV production is, by far, the most investigated one where many ML-based models have been proposed. Most research papers in this field are focused on point-forecast, though in the last several years some papers have also evaluated probabilistic forecasting that, in the authors' opinion, is the most interesting as provide the additional prediction interval associated with point forecasting. Due to the rise of DL and to the availability of ML frameworks such as Tensorflow or Pytorch, to cite a few, many DL models, mainly based on LSTM architectures, have proven to provide state of the art accuracy. These LSTM architectures mostly use historical values of PV production as well as environmental features and some techniques of an analogue ensemble. For probabilistic forecasting, methods based on some variation of Quantile regressions are the most common. Regarding forecasting horizon, the short-term horizon, from one hour to few days, is the most investigated. In this field, some ensemble methods such as LGBM have shown promising results. Only a few papers have investigated techniques such as TF and AML, techniques that could be of great usefulness in future applications, especially thinking on real-time applications. Regarding anomalies detection and fault analysis, the reviewed models employ electrical features and/or images (usually IFR or EL). Typically, SNNs, mainly MLP, RBF and ELMs, are employed in the first case while, as could be expected, DNNs, mainly CNNs, are more common for the detection of permanently visible faults. The models tested in the FDD field employ for most cases a simulated dataset and the more frequent faults taken into consideration are short circuits and partial shading. Regarding the metrics used to evaluate models for FDD, only a few papers correctly employ a broader range of metrics (e.g., F1, balanced accuracy and MCC) apart from the common accuracy that is adequate only for a balanced dataset. Transfer Learning is a useful technique that probably will be more and more adopted in FDD, at least for models employing images as features. In this field it would be advisable to promote sharing of public datasets (a common repository for images related to faults in PV, IFR and EL images). Ensemble models such as RF have seen only a few applications in the field of FDD but seem very promising. For all fields here analyzed, and more generally for research papers, it would be desirable to promote reproducible research results using technologies based on containers as Docker.  
Conceptualization, all authors.; methodology, all authors; validation, all authors; investigation, C.V. and S.F; data curation, C.V. and S.F; writing--original draft preparation, C.V. and S.F; writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.