PyPVRoof: a Python package for extracting the characteristics of rooftop PV installations using remote sensing data

TL;DR

PyPVRoof is a Python package that accurately extracts key characteristics of rooftop PV installations from remote sensing data, aiding PV deployment monitoring and power estimation.

Contribution

It introduces a comprehensive Python tool that consolidates and benchmarks methods for extracting PV system characteristics from remote sensing data.

Findings

Achieves high accuracy in estimating PV system tilt, azimuth, and capacity.

Provides a versatile tool covering various data availability scenarios.

Benchmark results available for reproducibility and comparison.

Abstract

Photovoltaic (PV) energy grows at an unprecedented pace, which makes it difficult to maintain up-to-date and accurate PV registries, which are critical for many applications such as PV power generation estimation. This lack of qualitative data is especially true in the case of rooftop PV installations. As a result, extensive efforts are put into the constitution of PV inventories. However, although valuable, these registries cannot be directly used for monitoring the deployment of PV or estimating the PV power generation, as these tasks usually require PV systems {\it characteristics}. To seamlessly extract these characteristics from the global inventories, we introduce {\tt PyPVRoof}. {\tt PyPVRoof} is a Python package to extract essential PV installation characteristics. These characteristics are tilt angle, azimuth, surface, localization, and installed capacity. {\tt PyPVRoof} is designed to cover all use cases regarding data availability and user needs and is based on a benchmark of the best existing methods. Data for replicating our accuracy benchmarks are available on our Zenodo repository \cite{tremenbert2023pypvroof}, and the package code is accessible at this URL: \url{https://github.com/gabrielkasmi/pypvroof}.