Harmonised global datasets of wind and solar farm locations and power

The methodology to produce these data is described in the accompanying paper:Dunnett, S., Sorichetta, A., Taylor, G. <i>et al</i>. Harmonised global datasets of wind and solar farm locations and power. <i>Sci Data</i> <b>7</b>, 130 (2020). https://doi.org/10.1038/s41597-020-0469-8Abstract<i>Energy systems need decarbonisation in order to limit global warming to within safe limits. While global land planners are promising more of the planet’s limited space to wind and solar photovoltaic, there is little information on where current infrastructure is located. The majority of recent studies use land suitability for wind and solar, coupled with technical and socioeconomic constraints, as a proxy for actual location data. Here, we address this shortcoming. Using readily accessible OpenStreetMap data we present, to our knowledge, the first global, open-access, harmonised spatial datasets of wind and solar installations. We also include user friendly code for enabling users to easily create newer versions of the dataset. Finally, we include first order estimates of power capacities of installations. We anticipate this data will be of widespread interest within global studies of the future potential and trade-offs associated with the global decarbonisation of energy systems.</i>Data overviewThe datasets represent three different formats of the following data in WGS 1984 and Eckert IV projections:Global wind turbines in April 2020 clustered using a neighbourhood distance of 800mGlobal solar installations in April 2020 clustered using a neighbourhood distance of 400mThe formats comprise:ESRI geodatabase (<i>*.gdb</i>): proprietary geospatial file format for use with ArcGIS that contains two layers in the same file, one for solar and one for wind;Geopackages (<i>*.gpkg</i>): open source geospatial file format that can work with a variety of software and can store different geometries natively;Comma-delimited files (<i>*.csv</i>): the simplest format, these files represent the centroid of each data record, <i>i.e.</i> point data, as X and Y coordinates in a data table.Also included is an archive folder, <i>analysis</i>, that contains four R scripts and accompanying data used for acquiring and processing the data.Sharing and accessData adapted or built on OpenStreetMap data are required to be distributed under the same licence. These data are therefore made available under the Open Data Commons Open Database License (ODbL). Personal <i>figshare</i> accounts cannot currently present data under this licence so the data are currently (incorrectly) presented under a CC0 licence as a stopgap until this changes. More information on OpenStreetMap data and use of data can be found here.The data should be cited as follows:Dunnett, S. Harmonised global datasets of wind and solar farm locations and power. <i>figshare</i>. Dataset. https://doi.org/10.6084/m9.figshare.11310269 (2020)

本数据集的制作方法详见伴随发表的论文：Dunnett S、Sorichetta A、Taylor G 等人。《风电场与太阳能电站位置及功率的全球统一数据集》，发表于《Scientific Data》（Sci Data），2020年，第7卷，第130页。DOI: 10.1038/s41597-020-0469-8

【摘要】为将全球温升控制在安全阈值内，能源系统亟需实现脱碳。当前全球土地规划者正计划将地球有限的土地更多分配给风电与光伏电站，但现有公开信息中几乎没有当前风电与光伏基础设施的实际分布数据。近期多数相关研究均以风电与光伏的土地适宜性，结合技术与社会经济约束条件，作为实际场址数据的替代指标。本研究针对这一研究缺口展开工作：我们利用易于获取的开放街道地图（OpenStreetMap）数据，构建了据我们所知全球首套开放获取、统一标准的风电与光伏电站空间数据集。同时配套提供了便于用户自主生成数据集新版本的易用代码，并首次给出了各电站装机容量的一阶估算值。我们预期本数据集将在全球能源系统脱碳的未来潜力与权衡取舍的相关全球研究中得到广泛应用。

【数据概览】本数据集包含以下两类数据的三种不同格式，采用WGS 1984与Eckert IV两种投影坐标系：
1. 2020年4月全球风电涡轮机数据，采用800米邻域距离进行聚类；
2. 2020年4月全球光伏电站数据，采用400米邻域距离进行聚类。

数据集支持以下三种格式：
1. ESRI地理数据库（*.gdb）：适用于ArcGIS的专有地理空间文件格式，单个文件中包含光伏与风电两个数据图层；
2. 地理包（*.gpkg）：开源地理空间文件格式，可兼容多种软件，原生支持存储多种几何类型；
3. 逗号分隔值文件（*.csv）：最简格式，以数据表中的X、Y坐标形式存储每条数据记录的质心，即点数据。

此外还包含一个名为analysis的归档文件夹，内含用于数据获取与处理的4个R脚本及配套数据。

【共享与获取】基于开放街道地图（OpenStreetMap）数据修改或构建的数据集，需遵循原许可协议进行分发。因此本数据集采用开放数据共享开放数据库许可（Open Data Commons Open Database License, ODbL）进行发布。由于个人figshare账户目前无法展示该许可下的数据集，本数据集暂以CC0许可进行公开（此为临时措施，后续将修正）。更多关于开放街道地图数据及数据使用的信息可参阅此处。

本数据集的引用格式如下：Dunnett S. 《风电场与太阳能电站位置及功率的全球统一数据集》，figshare，数据集，https://doi.org/10.6084/m9.figshare.11310269 (2020)

搜集汇总

背景与挑战

背景概述

该数据集提供了2020年4月全球风力和太阳能发电设施的协调空间数据，包括位置和功率估计，支持多种格式以适应不同分析需求。数据基于OpenStreetMap构建，遵循开放许可，适用于能源系统脱碳潜力研究。