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• Spectrally resolved DNI was tested for an entire year in Beijing. • The spectra can be represented in five-dimensional data by an autoencoder. • Hierarchical clustering was adopted to obtain ten reference spectra in the case. • Novel local reference spectra can characterize solar resources throughout the year. • The superiority of the new reference spectra over ASTM AM1.5D was verified. 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. [ABSTRACT FROM AUTHOR]

Fine description for regional solar spectra has always been critical for improving solar energy utilization. However, it is difficult and complex to implement due to large amount and high-dimensional characteristics of the measured solar spectra. To address the above challenges, this study proposed a data-driven method to describe regional solar spectra using 211,877 global horizontal irradiance spectra in Beijing area as case data. Firstly, the dimensions of each measured spectrum were reduced from 2,221 to six using the deep autoencoder. Then, all dimensionality-reduced measured spectra were categorized into five clusters using the agglomerative hierarchical clustering. The clustering results exhibited notable variations across different months and dates. Finally, the local reference spectra were determined based on clustering results, and their effects on the performance of typical photovoltaic materials were analyzed. The maximum mismatch factor for typical photovoltaic materials under local reference spectra can reach up to 21 %. The comparisons with standard spectrum highlighted that the superior capacity of local reference spectra to describe the configuration of regional solar energy. This study provides a novel insight into the fine description for regional solar spectra, which sets a new direction for innovation in photovoltaic technology and promotes the sustainable utilization of renewable energy. [ABSTRACT FROM AUTHOR]

Fine description for regional solar spectra has always been critical for improving solar energy utilization. However, it is difficult and complex to implement due to large amount and high-dimensional characteristics of the measured solar spectra. To address the above challenges, this study proposed a data-driven method to describe regional solar spectra using 211,877 global horizontal irradiance spectra in Beijing area as case data. Firstly, the dimensions of each measured spectrum were reduced from 2,221 to six using the deep autoencoder. Then, all dimensionality-reduced measured spectra were categorized into five clusters using the agglomerative hierarchical clustering. The clustering results exhibited notable variations across different months and dates. Finally, the local reference spectra were determined based on clustering results, and their effects on the performance of typical photovoltaic materials were analyzed. The maximum mismatch factor for typical photovoltaic materials under local reference spectra can reach up to 21 %. The comparisons with standard spectrum highlighted that the superior capacity of local reference spectra to describe the configuration of regional solar energy. This study provides a novel insight into the fine description for regional solar spectra, which sets a new direction for innovation in photovoltaic technology and promotes the sustainable utilization of renewable energy. [ABSTRACT FROM AUTHOR]