
Developed and delivered new solar potential estimation workflows in the dataforgoodfr/13_potentiel_solaire repository, focusing on building-level and school-focused energy planning. Leveraged Python, Pandas, and GeoPandas to integrate the PVGIS API, enabling automated data retrieval, rate limiting, and multi-level aggregation of solar irradiation data. Refactored legacy Jupyter Notebooks for improved code organization and maintainability, decoupling the PVGIS integration from existing pipelines to enhance reliability. Implemented persistent storage of results and enforced code quality through pre-commit checks. The work enabled reproducible, scalable solar energy calculations, supporting targeted planning for public facilities and optimizing data processing pipelines for geospatial analysis.
March 2025 — Dataforgoodfr/13_potentiel_solaire: Delivered PVGIS-based solar potential workflows enabling building-level estimations and targeted energy planning, along with a school-focused pipeline to aggregate and persist results. Major bugs fixed included cleanup of legacy PVGIS notebooks, decoupling the integration from the MSN pipeline, and enforcing CI hygiene through pre-commit checks. Overall impact: new capabilities for energy planning across public facilities with improved maintainability and reproducibility. Technologies demonstrated: API integration, data preparation, rate limiting, multi-level data aggregation, persistent storage, refactoring, and CI practices.
March 2025 — Dataforgoodfr/13_potentiel_solaire: Delivered PVGIS-based solar potential workflows enabling building-level estimations and targeted energy planning, along with a school-focused pipeline to aggregate and persist results. Major bugs fixed included cleanup of legacy PVGIS notebooks, decoupling the integration from the MSN pipeline, and enforcing CI hygiene through pre-commit checks. Overall impact: new capabilities for energy planning across public facilities with improved maintainability and reproducibility. Technologies demonstrated: API integration, data preparation, rate limiting, multi-level data aggregation, persistent storage, refactoring, and CI practices.

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