Spectral-matching-ratio modelling based on ANNs and atmospheric parameters for the electrical characterization of multi-junction concentrator PV systems

Abstract

One of the most critical issues to evaluate the performance of multi-junction (MJ) concentrator photovoltaic (CPV) systems is related to its spectral dependence. The spectral matching ratio (SMR) index is nowadays widely used to evaluate the spectral impact on CPV systems. The limitation of the present models devoted to estimating the SMR is related to the difficulty of obtaining high-quality data of aerosols and water vapour. This paper aims to fill this gap by introducing a novel approach based on commonly available variables in atmospheric stations and/or databases. In particular, the impact of aerosols has been quantified trough the ratio DNI/GNI (i.e. direct and global normal irradiances), while the impact of water vapour has been quantified through the air temperature (Tair) and relative humidity (Hr). Due to the complexity for finding appropriate relationships between these variables and the SMR indexes, an artificial neural network (ANN)-based model has been used. The model shows a high quality in the evaluation of the spectral performance of MJ CPV systems through the estimation of the SMR indexes, with a correlation coefficient ranging from 0.79 to 0.98, a Root Mean Square Error ranging from 2.32% to 4.32% and a Mean Bias Error around 0%.