document: In addition, the prediction of solar cell’s temperature is very important for the electrical characterisation of CPV modules. Rodrigo et al. (2014) reviewed various methods for the calculation of the cell temperature in High Concentrator PV (HCPV) modules. The methods were categorised based on: (1) heat sink temperature, (2) electrical parameters and (3) atmospheric parameters. The first two categories are based on direct measurements of CPV modules in indoor or outdoor experimental setups and presented the highest degree of accuracy (Root Mean Square Error (RMSE) 1.7–2.5K). Most of the methods reviewed by Rodrigo et al. (2014) calculate the cell temperature at open-circuit conditions. Methods that predict the cell temperature at maximum power point (MPP) operation offer a more realistic approach since they include the electrical energy generation of the solar cells (i.e. real operating conditions); Yandt et al. (2012) described a method predicting the cell temperature at MPP based on electrical parameters and Fernández et al. (2014b) based on heat sink temperature with absolute RMSE 0.55–1.44K. Fernández et al. (2014a) also proposed an artificial neural network model to estimate the cell temperature based on atmospheric parameters and an open-circuit voltage model based on electrical parameters (Fernandez et al., 2013a) offering good accuracy (RMSE 3.2K and 2.5K respectively (Rodrigo et al., 2014)). The main disadvantage of the aforementioned methods is that an experimental setup is required to obtain the parameters used for the cell temperature calculation.
extractive_keyphrases: artificial neural network model calculate the cell temperature calculation of the cell temperature cell cpv electrical energy generation hcpv high concentrator pv maximum power point mpp prediction of solar cell’s temperature solar cells
abstractive_keyphrases: cell temperature calculation estimate the cell temperature open-circuit conditions open-circuit voltage model predicting the cell temperature at mpp solar cell