When2Heat Heating Profiles

Simulated hourly country-aggregated heat demand and COP time series. This dataset comprises national time series for representing building heat pumps in power system models. The heat demand of buildings and the coefficient of performance (COP) of heat pumps is calculated for 28 European countries from 2008 to 2023 in an hourly resolution. Heat demand time series for space and water heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. The profiles are year-wise scaled to 1 TWh each. For the years 2008 to 2015, the data is additionally scaled with annual statistics on the final energy consumption for heating. COP time series for different heat sources – air, ground, and groundwater – and different heat sinks – floor heating, radiators, and water heating – are calculated based on COP and heating curves using reanalysis temperature data, spatially aggregated with respect to the heat demand, and corrected based on field measurements. All data processing as well as the download of relevant input data is conducted in python and pandas and has been documented in the Jupyter notebooks linked below. Please also consider and cite our Data Descriptor of the original dataset as well as our Working Paper at on recent updates and extensions of the dataset.本数据集为模拟的逐小时国家聚合式热负荷与热泵性能系数（Coefficient of Performance，COP）时间序列，包含用于电力系统模型中建筑热泵表征的国家级时间序列数据。本数据集针对28个欧洲国家，以逐小时分辨率计算了2008年至2023年间的建筑热负荷与热泵COP。用于空间供暖与生活热水的热负荷时间序列，通过将燃气标准负荷曲线与空间温度、风速再分析数据及人口地理数据相结合计算得到。各负荷曲线按年度分别缩放至1 TWh；针对2008年至2015年的数据，还会结合供暖终端能源消费的年度统计数据进行二次缩放。针对不同热源（空气、土壤、地下水）与不同热汇（地板供暖、散热器、生活热水）的COP时间序列，基于COP与供暖曲线，结合再分析温度数据计算得到，随后根据热负荷进行空间聚合，并基于实地测量数据完成修正。所有数据处理及相关输入数据的下载工作均通过Python与Pandas完成，相关过程已在下文链接的Jupyter笔记本中进行了完整记录。若需了解本数据集的最新更新与扩展内容，请一并参考并引用我们发布的原始数据集数据描述文档与相关工作论文。

搜集汇总

背景与挑战

背景概述

该数据集提供28个欧洲国家2008-2022年每小时建筑热需求和热泵COP时间序列，涵盖空间/热水加热需求及不同热源/散热系统的性能数据，适用于电力系统建模。数据通过多源数据融合计算产生，包含标准化和原始值，提供多种格式和粒度的下载选项。