PLANtoACT Task 2.1: Hourly profiles - Wind power
PLANtoACT タスク2.1:時間別プロファイル - 風力発電 (AI 翻訳)
Prina, Matteo Giacomo
🤖 gxceed AI 要約
日本語
欧州5地域の風力発電の正規化された時間別プロファイルを提供するデータセット。MERRA-2データとRenewables.ninja APIを用いて生成され、エネルギーシステムモデリングや地域計画に利用可能。
English
This dataset provides normalized hourly wind power generation profiles for five European regions, generated using MERRA-2 and Renewables.ninja. It supports energy system modeling and regional planning.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の再生可能エネルギー導入拡大や地域エネルギー計画に参考となる。特に、風力発電の変動性を考慮した系統計画や需給調整に活用できる。
In the global GX context
This dataset offers a replicable methodology for regional wind profiling, relevant to global renewable integration and energy planning, especially for regions with similar data needs.
👥 読者別の含意
🔬研究者:エネルギーシステムモデリングの研究者は、地域風力プロファイルの生成手法を参照できる。
🏢実務担当者:エネルギー計画担当者は、地域の風力発電の変動性を評価するためのデータとして利用できる。
🏛政策担当者:再生可能エネルギー導入目標の達成に向けた地域計画の策定に役立つ。
📄 Abstract(原文)
Regional Wind Power Profiles for the PLANtoACT Project Description This dataset contains normalized hourly wind power generation profiles developed for the PLANtoACT project (Task 2.2). The profiles represent the long-term wind generation behaviour of five European pilot regions and are intended for use in energy system modelling, renewable energy assessment, and regional energy planning. The dataset was generated using the Renewables.ninja API together with regional administrative boundaries. Hourly wind power time series were downloaded for multiple MERRA-2 grid points located within each region, aggregated into representative regional profiles, and 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 wind generation profiles for the following regions: Country Region Italy Lombardia Romania Alba Germany Oberland France Auvergne-Rhône-Alpes Portugal Porto Metropolitan Area Dataset Structure Each regional folder contains: File Description profile_final_8760h.csv Final normalized hourly wind profile for a standard (8760-hour) year profile_final_8784h.csv Final normalized hourly wind profile for a leap (8784-hour) year profile_final_8760h.txt Plain-text version of the 8760-hour profile profile_final_8784h.txt Plain-text version of the 8784-hour profile profile_aggregated_normalised.csv Aggregated multi-year normalized profile before correction heq_by_year.csv Equivalent full-load hours calculated for each simulated year grid_map.png MERRA-2 grid points used for the regional aggregation heq_comparison.png Comparison of annual equivalent full-load hours profile_final_plot.png Visualization of the final normalized profile The dataset also includes Normalized_Profiles_wind_2024.png which compares the normalized wind generation profiles across all study regions. Data Generation Methodology The regional wind profiles were generated according to the following workflow: Regional administrative boundaries were provided as GIS shapefiles. MERRA-2 grid points falling within each regional polygon were identified. Hourly wind power capacity factors were downloaded from the Renewables.ninja API for each grid point over a five-year period (2020–2024). Hourly time series from all selected grid points were aggregated to produce a representative regional profile. Annual equivalent full-load hours (Heq) were calculated for each grid point and for the aggregated profile. The aggregated profile was normalized using the maximum observed generation. A non-linear correction factor was applied to the most recent year (2024) to preserve the long-term average annual energy production while maintaining the hourly variability. Final normalized hourly profiles were exported for both standard (8760-hour) and leap-year (8784-hour) calendars. Data Format The profile files contain a single column: Column Description normalised Hourly normalized wind 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 maximum capacity factor. 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 GeoPandas Shapely SciPy Matplotlib Requests Hourly wind generation data were obtained using the Renewables.ninja API. Related Software The scripts used to generate this dataset are available from the associated GitLab repository: PLANtoACT Task 2.2 – Regional Wind 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. Authors Matteo Giacomo Prina Valentina D'Alonzo Citation If you use this dataset in your work, please cite both the Zenodo record and the associated software repository: PLANtoACT / task_2_1 / Wind Power Hourly Profiles · GitLab .
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/21825804first seen 2026-08-07 04:17:40
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