Spectra measurement and clustering analysis of global horizontal irradiance for solar energy application

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.