Electric and Heat Load Profiles, PV Generation, Temperature Data, and Stratified Thermal Storage Parameters for a Single-Family House in Northern Germany

This dataset consists of electric and heat load profiles, photovoltaic generation, temperature data, and stratified thermal storage parameters for a single-family house located in Northern Germany. The hourly electric load profile (in kW) was generated by using the Python package demandlib [1] from the Open Energy Modelling Framework (oemof) [2] with the assumption of an annual electric demand of 4919 kWh. The hourly temperature data (in ºC) was obtained from Deutscher Wetterdienst (DWD) [3] for a location in Northern Germany. With the temperature data and assuming 21402 kWh of annual heat demand, the hourly heat load profile (in kW) was obtained by using the Python package demandlib. The first timestep of the hourly electric load profile, heat load profile, photovoltaic generation, and temperature profile starts on January 1st, 00:00. To obtain the photovoltaic generation (normalized), PVGIS [4] was used. In addition, information about parameters of a stratified thermal storage is included, which is based on public data from the Python package oemof.thermal [5]. This dataset can be useful as input data for an energy system optimization model.本数据集涵盖德国北部一栋独栋住宅的电力与热负荷曲线、光伏发电数据、气温数据以及分层储热参数。该数据集的小时级电力负荷曲线（单位：千瓦，kW）通过开源能源建模框架（Open Energy Modelling Framework，oemof）[2]下的Python工具库demandlib[1]生成，设定年电力需求量为4919 kWh。小时级气温数据（单位：摄氏度，℃）源自德国国家气象局（Deutscher Wetterdienst，DWD）[3]的德国北部某站点观测数据。基于上述气温数据，并设定年热需求量为21402 kWh，通过Python工具库demandlib生成了小时级热负荷曲线（单位：千瓦）。电力负荷曲线、热负荷曲线、光伏发电数据与气温曲线的首个时间步均始于1月1日00:00。归一化光伏发电数据通过PVGIS[4]获取。此外，数据集还包含分层储热相关参数，该参数基于Python工具库oemof.thermal[5]的公开数据。本数据集可作为能源系统优化模型的输入数据使用。