Data mining techniques for novel local reference solar irradiance spectra calculations: A case study in Beijing

The established reference spectrum (ASTM AM1.5) based on mid-latitudes is still the most frequently used standard in design and simulation in various fields worldwide, however, the solar spectrum varies with climate and time, which significantly affects the evaluations of solar energy utilization. This study proposes a novel reference spectrum analysis method to establish several local reference spectra, in this case, based on 147,374 spectra in Beijing, which can characterize solar resources throughout the year. The dataset consists of spectra of direct normal irradiance, and a deep learning autoencoder is adopted to extract the intrinsic features of each spectrum for clustering. 10 clusters are finally divided by agglomerative hierarchical clustering, and 10 local reference spectra were identified from each cluster by correlation analysis. The results show that the shape of new reference spectra shifts to the short wave as the irradiance increases, and the average photon energy increases from 1.069 eV to 1.456 eV. The new local reference spectra are compared with the standard spectrum AM1.5D to show the influence of shape variations, and it reveals 61% maximum differences among photovoltaic applications. The superiority verified new reference spectra could be further used to optimize local solar technologies design and guide photovoltaic material development.

Automated summary

This study proposes a novel reference spectrum analysis method to establish local reference spectra based on 147,374 spectra in Beijing, which can characterize solar resources throughout the year. The deep learning autoencoder is adopted to extract intrinsic features of each spectrum for clustering and 10 clusters are divided by hierarchical clustering. The new local reference spectra show shape variations compared to the standard spectrum AM1.5D, with 61% maximum differences among photovoltaic applications.