Can We Reliably Improve the Robustness to Image Acquisition of Remote Sensing of PV Systems?
This work addresses the need for reliable monitoring of rooftop PV installations to support decarbonization, though it appears incremental in applying an existing attribution method to a specific domain.
The paper tackled the problem of unreliable remote sensing of rooftop photovoltaic (PV) systems due to sensitivity to image acquisition shifts, and proposed using the wavelet scale attribution method (WCAM) to improve robustness and increase trust in deep learning systems for monitoring clean energy integration.
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.