Investigation of Spectral Variations in Different Solar Photovoltaic Modules Using Spaceborne and In-Situ Hyperspectral Data

Abstract

Photovoltaic (PV) technology is critical to achieving a sustainable society. These PV systems are distributed over large areas, making it important to study their characteristics for applications such as solar power output modeling. The combination of satellite imagery, especially multispectral imagery, with machine learning models has proven to be an effective tool for locating PVs. However, PVs are typically treated as a single category in previous studies, despite the existence of different types with different important parameters such as energy conversion efficiency and lifetime. The objective of this research was to investigate the detailed spectral data of four PV types currently on the market to enable their differentiation in hyperspectral satellite data. Spectral samples were collected using a handheld spectrometer and the hyperspectral imager suite satellite hyperspectral sensor. There were notable differences in the reflectance characteristics of the four PV types. The presence of vegetation around the PVs can affect the satellite-encoded spectral signature. Four spectral indices (SIs) were defined based on the spectral characteristics of each PV type, and the discriminability of the different PV types based on the proposed indices was evaluated using a statistical metric. The potential to discriminate between different solar PV types was demonstrated through the combined use of satellite hyperspectral data and SIs.