# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 7 Other Applications in the PV Field
The proposed ANN approach proved that using nanofluid/nano-PCM enhanced the electrical efficiency from 8.07% to 13.32% and its thermal efficiency reached 72%. In [119] authors examined the feasibility of several ML techniques to forecast the energetic performance of a building-integrated PV/T (BIPV/T) collector. In particular, they tested the following techniques: multiple linear regression (MLR), MLP, RBF regressor, sequential minimal optimization improved support vector machine (SMO Improved), lazy.IBK, RF and random tree (RT). Moreover, it implements the performance evaluation criteria (PEC) to evaluate the system's performance from the perspective of exergy. The results for the testing dataset showed the RF model is superior to other proposed models with an RMSE equal to 0.8153 compared with an RMSE of 18.9966 for MLR, 4.5751 for MLP, 8.9168 for RBF Regressor, 22.7223 for SMO Improved, 8.4233 for lazy.IBK and 2.115 for RT. The prediction of thermal efficiency of PV/T setups is studied in [120], regarding input temperature, recirculation flow rate and solar irradiation by modifying MLP-ANN, ANFIS and least-squares SVM (LSSVM) approaches. An experimental dataset of 100 data points (empirical measurements performed on a fabricated water-cooled PV/T setup) has been used. Graphical and statistical methods were employed to determine the credibility of the proposed models in accurate prediction of the thermal efficiency. The proposed ANN model provided the best performance compared to ANFIS and LSSVM models due to the MSE and R2 values of 0.009 and 1.00, respectively. In [121] ML methods of ANNs (MLP-ANN and RBF-ANN), LSSVM and ANFIS have been used for advancing prediction models for the thermal performance of a PV/T solar collector. As the input variables for the proposed models, authors have considered: inlet temperature, flow rate, heat, solar radiation and the sun heat. The electrical efficiency yield has been used as the output. The data set has been extracted through experimental measurements from a novel solar collector system. Results show that the proposed LSSVM model outperforms other models with an R2 equal to 0.987 and an MSE equal to 0.004. Further, in [121] the sensitivity analysis demonstrates that the water inlet temperature has the most significant relevancy factor and therefore it is the parameter that most affects the efficiency of the PV/T system. Finally, in [122] a comparison study of prediction data system of PV/T output power by using ANN techniques considered published studies in data sets for the years 2008-2017. The results show that ANN models are the most suitable for the prediction of global solar radiation. The presented study offers a cheap and easy method for implementing PV models and choosing the desired location for providing good performance for the system. Several models were used to simulate and measure the production of energy in solar cells including ANN such as MLP, Bayesian NN (BNN), RNN (Recurrent NN), Generalized Feed-Forward (GFF), SVM, Self-organization feature map (SOFM) and LSTM. To obtain the most significant benefit from the best model, several mathematical coefficients have been adopted to determine the validity of accurate results such as MAPE, MSE, RMSE, MBE, Mean Percentage Error (MPE) and R2. The comparative study can clarify the best implementation method and address the scope of weakness in any of the proposed models based on the results of scientific verification and operation. Furthermore, in [123; 124] ML is used to improve the efficiency of control algorithms used in PV-storage systems. In particular, in [123] an algorithm using ML to effectively control PV-storage systems has been developed. The algorithm uses an offline policy planning stage and an online policy execution stage. In the planning stage, a suitable machine learning technique is used to generate models that map states (inputs) and decisions (outputs) using training data. In the execution stage, the model generated by the ML algorithm is then used to generate fast real-time decisions.