PLANtoACT Task 2.1: Hourly profiles - Solar power

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides high-resolution solar PV profiles for energy system modeling and scenario analysis.

🏢実務担当者:Useful for regional energy planning and renewable energy project assessment.

🏛政策担当者:Supports evidence-based renewable energy policy and grid integration planning.

Regional Solar PV Power Profiles for the PLANtoACT Project This dataset contains normalized hourly solar photovoltaic (PV) generation profiles developed for the PLANtoACT project (Task 2.2). The profiles represent the long-term PV generation behaviour of five European pilot regions, for four representative PV deployment categories, and are intended for use in energy system modelling, renewable energy assessment, and regional energy planning. The dataset was generated using the PVGIS (Photovoltaic Geographical Information System) API of the European Commission's Joint Research Centre. Ten years of hourly PV power time series were downloaded for each region and deployment category, combining one or more representative mounting sub-configurations (tilt, azimuth, and tracking type) into an aggregated regional profile, which was then normalized while preserving the long-term equivalent full-load hours (Heq). The dataset accompanies the scripts available in the corresponding GitLab repository. Study Regions The dataset contains solar PV generation profiles for the following regions: Country Region Italy Lombardia Romania Alba Iulia Germany Oberland France Auvergne-Rhône-Alpes Portugal Porto PV Deployment Categories For each region, profiles are provided for four PV deployment categories, each built from one or more mounting sub-configurations: Category Sub-configurations System losses Rooftop Residential PV 30° tilt, SE / S / SW azimuth (fixed) 18% Rooftop Commercial PV 30° tilt, SE / S / SW azimuth (fixed) 14% Utility-Scale Single-Axis Tracker PV Horizontal N–S single-axis tracker 10% AgriPV Horizontal N–S single-axis tracker + 30° tilt, S azimuth (fixed) 14% Dataset Structure Each regional folder contains one sub-folder per PV deployment category, with the following files: File Description pvgis_<subcat>.csv Raw hourly PVGIS download (1 kWp), one file per mounting sub-configuration heq_by_year.csv Equivalent full-load hours calculated for each simulated year, per sub-configuration and aggregated profile_aggregated_normalised.csv Aggregated 10-year normalized profile before correction profile_final_8760h.csv / .txt Final normalized hourly profile for a standard (8760-hour) year profile_final_8784h.csv / .txt Final normalized hourly profile for a leap (8784-hour) year profile_metadata.json Metadata (category, location, year range, average and physical Heq) heq_comparison.png Comparison of annual equivalent full-load hours vs the 10-year average profile_final_plot.png Visualization of the final normalized profile (full year + representative January/July weeks) The dataset also includes, at the top level: comparison_heq.png , comparing average equivalent full-load hours across all five regions and all four PV categories; Normalized_Profiles_<Category>_2024.png (one per category), comparing the normalized PV generation profiles across all study regions. Data Generation Methodology The regional solar PV profiles were generated according to the following workflow: A representative coordinate pair (latitude/longitude) was defined for each of the five pilot regions. For each region and PV deployment category, hourly PV power output was downloaded from the PVGIS seriescalc API (SARAH-3 database) for a nominal 1 kWp crystalline-silicon system, for each mounting sub-configuration (tilt, azimuth, fixed or single-axis tracking) and over a ten-year period. Sub-configuration time series were summed on a common hourly index and divided by the number of sub-configurations to obtain an aggregated regional profile. Annual equivalent full-load hours (Heq) were calculated for each sub-configuration and for the aggregated profile, for every simulated year. The aggregated 10-year profile was normalized using its own maximum observed generation. A non-linear correction factor was applied to the most recent year to preserve the 10-year average annual equivalent full-load hours while maintaining the hourly and seasonal variability of that year. Final normalized hourly profiles were exported for both standard (8760-hour) and leap-year (8784-hour) calendars, the latter obtained by duplicating the last 24 hours of the standard-year profile. Data Format The profile_final_8760h / profile_final_8784h files contain a single column: Column Description normalised Hourly normalized PV power generation (dimensionless, ranging from 0 to 1) Each row represents one hour of the year. The annual energy production can be reconstructed by multiplying the normalized profile by the corresponding regional and category-specific physical equivalent full-load hours (Heq), reported in profile_metadata.json and heq_by_year.csv . Intended Applications The dataset is intended for: Energy system modelling Renewable energy scenario analysis Regional energy planning Capacity expansion modelling Long-term electricity system simulations Sector coupling studies Academic research Software The dataset was generated using Python together with the following libraries: pandas NumPy SciPy Matplotlib Requests Hourly solar PV generation data were obtained using the PVGIS API of the European Commission's Joint Research Centre. Related Software The scripts used to generate this dataset are available from the associated GitLab repository: PLANtoACT Task 2.2 – Regional Solar PV Power Profile Generation Funding This work was developed within the PLANtoACT project. The PLANtoACT project has received funding from the European Union's LIFE Programme under Grant Agreement No. 101214506 (LIFE-2024-CET), managed by the European Climate, Infrastructure and Environment Executive Agency (CINEA). Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or CINEA. Neither the European Union nor CINEA can be held responsible for them. Citation If you use this dataset in your work, please cite both the Zenodo record and the associated software repository: PLANtoACT / task_2_1 / Solar PV Power Hourly Profiles · GitLab, https://gitlab.inf.unibz.it/plantoact/task_2_1/solar_power_hourly_profiles.

• Zenodo https://zenodo.org/records/21833902first seen 2026-08-08 04:14:58