Teaching and popularization

Teaching

2022-2023

Group projects

About 20h of teaching load to a group of 2nd year students of engineering degree at ENSAE Paris ("groupe de statistiques appliquées"). The project title was "Deep learning for detecting individual solar systems from aerial images". The students tasks were to carry out a short literature review, define the fundamental notions of machine learning and deep learning (empirical risk minimization, perceptron, neural network, convolutional layers, CNN) and to implement a custom-made and a ready-to-use neural network for binary classification on BDAPPV. The students leveraged PyTorch to train and evaluate the classification model. The project repository can be accessed here and the students' implementation here.

About 20h of teaching load to a group of 1st year students of the engineering degree at Mines Paris ("projet d'informatique"). The students task was to implement a website to visualize the outputs of DeepPVMapper (in a similar fashion as this website). The main tasks were to implement an architecture with a client and a host based on Flask, to implement spatial indexing and preprocessing of the raw data to minimize the loading times and to propose some complementary statistics (e.g. the number of systems or the cumulated installed capacity per city). The project repository can be accessed here. This project was co-supervised with R. Jolivet.

2021-2022

Internships

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 PyPVRoof. PyPVRoof is a Python package to extract essential PV installation characteristics. These characteristics are tilt angle, azimuth, surface, localization, and installed capacity. 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 DOI , and the package code is accessible at this URL.

The main tasks of the internship included an extensive literature review of the existing works in the field of extracting characteristics of PV systems, a benchmark of these methods and an implementation of new methods (e.g. Theil-Sen regression). All methods were extensively compared both in terms of accuracy and in terms of computational cost on BDAPPV. Yann put a particular emphasis on the environmental impact of his coding practices and the methods that he implemented. The idea to discuss the environmental impact of DeepPVMapper (appendix A of the thesis manuscript) was brought by him. This internship was co-supervised with L. Dubus.

Talks and popularization

Press articles