# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 7 Other Applications in the PV Field  
In few cases, ML algorithms have been used to improve the performance of concentrating PV (CPV). In particular, in [115] authors studied a Random-Forest (RF) model for the temperature analysis of two different triple-junction solar cells mounted on an experimental CPV system. The cell temperature evaluation is a basic parameter to determine the energy production of a CPV/T system. Moreover, an ANN model and an LRM have been also studied to compare the RF model results in terms of absolute error and fit capability. The RF model to evaluate the performances of a CPV system from electric and thermal presents the lowest values for RMSE, MAE and MAPE. In particular, RMSE is 1.95 \({}^{\circ}\)C, the MAE is 1.17 \({}^{\circ}\)C and the MAPE is 3.67%. These values are two or three times lower than the LR and ANN models results. However, it should be noted that the ANN model shows better statistical results with respect to the LRM. This proves that a non-linear method represents a better solution than a linear one for the cell temperature evaluation. The good forecast capability of the RF technique is also proved by the values of the goodness of fit(R2). In particular, the estimated values are 0.95, 0.79 and 0.76, respectively, for the RF, ANN and LRM models. Finally, the RF model constitutes the best method both in terms of absolute error and fit capability. Another paper where ML algorithms are used in the field of CPV is [116]. The authors developed four machine-learning algorithms (support vector machine, ANN, kernel, nearest-neighbour and deep learning) to predict the power outputs of a CPV system. The authors concluded that all machine learning algorithms used in the paper can successfully predict PV module output power. However, the SVM algorithm performed reasonably well throughout the day. The k-NN algorithm shows a prediction trend similar to SVM at the beginning of the observations. However, it is possible to say that this algorithm gives a better result than SVM, especially in the initial and final observations. As the predictions with ANN are analyzed, it is seen that this algorithm is successful in predicting peak points as in the SVM algorithm. On the other hand, the DL approach predicted power output higher than the measured value. On the other hand, the reason for the higher deviation in the DL algorithm is probably related to the availability of data. In [117] an RBFNN is used in the field of CPV. More specifically, it is used to predict the output power of a high CPV (HCPV) facility. The RBFNN has been designed using MATLAB. Two coefficients have been used to verify the accuracy of the adopted solution, the RMSE and the R2. The results were compared to those obtained by the ASTM E-2527 model using the same dataset. Results have been divided for sunny and cloudy days, obtaining an RMSE of 3.3 kW and 4 kW, respectively, in the case that the ASTM model is used. In the case of R, BFNN the RMSE is equal to 1.3 kw for sunny days and 2.24 kW for cloudy days. The obtained value of R2 is 0.322 for sunny days and 0.339 for cloudy days in the case of the ASTM model. Another application in the PV field where ML has been used to improve the performance of the system is PV/T hybrid systems. They consist of conventional thermal collectors with an absorber covered by a PV layer. The PV modules produce electricity and simultaneously the absorbed thermal energy is transported away by the working fluids. In [118] different PV/T systems (conventional PV, water-based PV/T, water-nanofluid PV/T and nanofluid/nano-PCM) under the same conditions and environment have been tested using one ANN-based MLP system. The parameters used in simulating the neural models were input parameters such as Solar Irradiation and Ambient Temperature, whereas the output parameters were PV/T Current (A), PV/T Voltage (V), PV/T Electrical Efficiency (%) and PV/T Thermal Efficiency (%). The MLP based on the backpropagation algorithm using momentum learning function was used.