Abstract
This paper presents an interactive pipeline for geospatial mapping of photovoltaic (PV) energy yield, transforming a trained weather-to-production model into a spatial decision tool. The approach follows two stages. First, a predictive model is trained on reference PV data using selected meteorological features and consistent preprocessing (imputation and scaling). Second, the model is deployed in a mapping module that retrieves daily weather data for a chosen location and surrounding grid from the NASA POWER API, applies the saved preprocessing, and estimates PV yield for each point. Results are visualized as a heatmap, with point-level predictions and basic economic indicators based on user-defined system capacity and energy price. A~case study shows that the “train once, infer across space” approach enables fast and reproducible assessment of PV yield variability driven by weather conditions, while noting limitations such as site-specific effects and the need for multi-site calibration.
Paper Type
Full Paper
DOI
10.62036/ISD.2026.74
Interactive Geospatial Mapping of Photovoltaic Energy Yield Using a Learned Weather-to-Production Model
This paper presents an interactive pipeline for geospatial mapping of photovoltaic (PV) energy yield, transforming a trained weather-to-production model into a spatial decision tool. The approach follows two stages. First, a predictive model is trained on reference PV data using selected meteorological features and consistent preprocessing (imputation and scaling). Second, the model is deployed in a mapping module that retrieves daily weather data for a chosen location and surrounding grid from the NASA POWER API, applies the saved preprocessing, and estimates PV yield for each point. Results are visualized as a heatmap, with point-level predictions and basic economic indicators based on user-defined system capacity and energy price. A~case study shows that the “train once, infer across space” approach enables fast and reproducible assessment of PV yield variability driven by weather conditions, while noting limitations such as site-specific effects and the need for multi-site calibration.
Recommended Citation
Szczepanik, T. & Zalasiński, M.(2026). Interactive Geospatial Mapping of Photovoltaic Energy Yield Using a Learned Weather-to-Production Model. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.74