Can We Reliably Improve the Robustness to Image Acquisition of Remote Sensing of PV Systems? (Papers Track)

Gabriel Kasmi (Mines Paris - PSL); Laurent Dubus (RTE France); Yves-Marie Saint-Drenan (Mines Paris - PSL); Philippe Blanc (Mines Paris - PSL)

Paper PDF Poster File Recorded Talk NeurIPS 2023 Poster Cite
Computer Vision & Remote Sensing


Photovoltaic (PV) energy is crucial for the decarbonization of energy systems. Due to the lack of centralized data, remote sensing of rooftop PV installations is the best option to monitor the evolution of the rooftop PV installed fleet at a regional scale. However, current techniques lack reliability and are notably sensitive to shifts in the acquisition conditions. To overcome this, we leverage the wavelet scale attribution method (WCAM), which decomposes a model's prediction in the space-scale domain. The WCAM enables us to assess on which scales the representation of a PV model rests and provides insights to derive methods that improve the robustness to acquisition conditions, thus increasing trust in deep learning systems to encourage their use for the safe integration of clean energy in electric systems.

Recorded Talk (direct link)