Automatic Solar PV Array Identification

What is it about?

Solar photovoltaic (PV) installation and profiling information are critical in smart grid and smart city management. However, existing approaches suffer from low detection accuracy, limiting their effectiveness. This paper identifies the key challenges contributing to these inaccuracies and proposes SolarDetector, a novel open-source system designed to automatically and accurately detect and profile distributed solar PV arrays without incurring additional costs. By leveraging advanced methodologies, SolarDetector enhances detection precision, offering a valuable tool for large-scale solar PV monitoring and management.

Featured Image

Photo by Daniele La Rosa Messina on Unsplash

Photo by Daniele La Rosa Messina on Unsplash

Why is it important?

We find that prior machine learning (ML) and deep learning (DL) approaches are typically trained on very high-resolution (VHR) images, which are expensive and not universally available, limiting their scalability. Prior hybrid approaches that separate rooftop object segmentation from the detection process often fail to capture critical rooftop contextual information, leading to missed solar PV arrays. Furthermore, since most existing ML-based, DL-based, and hybrid methods detect solar PV arrays at the image contour level, they struggle to accurately and reliably identify multi-panel solar deployments.

Perspectives