Module operating temperature model for free-standing photovoltaic system in malaysia using principal component regression

Module temperature (MT) of photovoltaic (PV) system is reported to be one of the highest contributors in derating PV power generation. Due to the significant effect, many studies have been carried out in producing accurate MT model. Different types of modeling technique such as simple linear regression (SLR), multiple linear regression (MLR) and artificial neural networks (ANN) have been established. Though all the techniques have been successful in modeling, but it can still face modeling problems such as multicollinearity. However, the ability of Principal Component Analysis (PCA) in forming new variables from combination of the original variables, enables the MT model to be improved and eliminated the multicollinearity problem. The aim of this paper is to execute the remedial measures using PCA in overcoming the multicollinearity issue among ambient temperature (AT) and relative humidity (RH) in MT prediction and to develop a multiple linear MT model for free-standing grid connected PV (FS GCPV) system in Malaysia. Combination of PCA and MLR techniques called Principal Component Regression (PCR) was applied in developing the MT model. PCA analysis shows that there is no multicollinearity problem among AT and RH. Hence, PCR model have been successfully established for MT model of FS GCPV system in Malaysia.

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