PV Generation and Consumption Dataset of an Estonian Residential Dwelling
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This is a Residential PV generation and consumption data set from an Estonian house. At the time of submission, one year (2023) of data was available. The data was logged at a 10-second resolution. The untouched dataset can be found in the raw data folder, which is separated month-wise. A few missing points in the dataset were filled with a simple KNN algorithm. However, improved data imputation methods based on machine learning are also possible. To carry out the imputing, run the scripts in the script folder one by one in the numerical serial order (SC1..py, SC2..py, etc.). Data Descriptor (Scientific Data): https://doi.org/10.1038/s41597-025-04747-w General Information: Duration: January 2023 – December 2023 Resolution: 10 seconds Dataset Type: Aggregated consumption and PV generation data Logging Device: Camile Bauer PQ1000 (×2) Load/Appliance Information: 5 kW Rooftop PV array connected to AC Bus via 4.2kW 3-ϕ Inverter Air conditioner: 0.44 kW (Cooling), 0.62 kW (Heating) Air to Water (ATW) Heat Pump: 2.5kW (Cooling), 2.6 kW (Heating) ATW Cylinder unit: 0.21 kW (Controller), 9 kW (Booster Heater) Microwave oven: 0.9 kW Coffee Maker: 1 kW Cooktop Hot Plate: 4.6 kW TV: 0.103 kW Vacuum Cleaner: 1.5 kW Ventilation: 0.1 kW Washing Machine: 2.2 kW Electric Sauna: 10 kW Lighting: 0.25 kW EV charger: 2.4 kW 1-ϕ Measurement Points: PV converter-side current transformer, potential transformer (Measurement of PV generation). Utility meter-side current transformer, potential transformer (Measurement of power exchange with the grid). Measured Parameters: Per-phase mean power recorded within the sampling period Per-phase Minimum power recorded within the sampling period Per-phase maximum power recorded within the sampling period Quadrant-wise mean power recorded within the sampling period (1st + 3rd), (2nd + 4th) Quadrant-wise minimum power recorded within the sampling period (1st + 3rd), (2nd + 4th) Quadrant-wise maximum power recorded within the sampling period (1st + 3rd), (2nd + 4th) mean power Factor recorded within the sampling period Minimum power Factor recorded within the sampling period Maximum power Factor recorded within the sampling period System Voltage Minimum system Voltage Maximum system Voltage Mean Voltage between phase and neutral Minimum voltage between phase and neutral Maximum voltage between phase and neutral Zero displacement voltage 4-wire systems (mean, min, max) Script Description: SC1_PV_auto_sort.py : This fixes timestamp continuity by resampling at the original sampling rate for PV generation data. SC2_L2_auto_sort.py : This fixes timestamp continuity by resampling at the original sampling rate for meter-side measurement data. SC3_PV_KNN_impute.py : Filling missing data points by simple KNN for PV generation data. SC4_L2_KNN_impute.py : Filling missing data points by simple KNN for meter-side measurement data. SC5_Final_data_gen.py : Merge PV and meter-side measurement data, and calculate load consumption. The dataset provides all the outcomes (CSV files) from the scripts. All processed variables (PV generation, load, power import, and export) are expressed in kW units. Update: 'SC1_PV_auto_sort.py' & 'SC2_L2_auto_sort.py' are adequate for cleaning up data and making the missing point visible. 'SC3_PV_KNN_impute.py' & 'SC4_L2_KNN_impute.py' work fine for short-range missing data points; however, these two scripts won't help much for missing data points for a longer period. They are provided as examples of one method of processing data. Future updates will include proper ML-based forecasting to predict missing data points. Funding Agency and Grant Number: European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 955614. Estonian Research Council under Grant PRG1086. Estonian Centre of Excellence in Energy Efficiency, ENER, funded by the Estonian Ministry of Education and Research under Grant TK230.
本数据集为爱沙尼亚某住宅的光伏（Photovoltaic, PV）发电与用电数据。投稿时可获取的数据集涵盖2023一整年的采集数据，采样分辨率为10秒。未经过处理的原始数据集存储于raw data文件夹内，该文件夹按月份进行分类存储。数据集中存在少量缺失值，已通过简单K近邻（K-Nearest Neighbors, KNN）算法完成补全；此外，也可采用基于机器学习的优化数据插补方法进行处理。如需执行数据插补流程，请按数字序列顺序依次运行script文件夹内的脚本文件（即SC1..py、SC2..py等）。 数据描述文档（Scientific Data期刊）：https://doi.org/10.1038/s41597-025-04747-w 基本信息： 数据采集周期：2023年1月—2023年12月 数据采样分辨率：10秒 数据集类型：聚合式用电与光伏发电数据 采集设备：Camile Bauer PQ1000电能质量分析仪（×2） 负载/电器参数： - 5 kW屋顶光伏阵列：通过4.2 kW三相逆变器接入交流母线 - 空调：制冷功率0.44 kW，制热功率0.62 kW - 空气源热泵（Air to Water, ATW）：制冷功率2.5 kW，制热功率2.6 kW - ATW储水罐单元：控制器功率0.21 kW，辅助加热器功率9 kW - 微波炉：0.9 kW - 咖啡机：1 kW - 灶台加热板：4.6 kW - 电视机：0.103 kW - 吸尘器：1.5 kW - 通风系统：0.1 kW - 洗衣机：2.2 kW - 电桑拿房：10 kW - 照明系统：0.25 kW - 电动汽车充电器：单相2.4 kW 测量点位： - 光伏逆变器侧电流互感器、电压互感器：用于光伏发电量测量 - 公用计量表侧电流互感器、电压互感器：用于测量与电网的功率交互情况 测量参数： - 采样周期内记录的各相平均功率 - 采样周期内记录的各相最小功率 - 采样周期内记录的各相最大功率 - 采样周期内记录的按象限划分的平均功率（第1+3象限、第2+4象限） - 采样周期内记录的按象限划分的最小功率（第1+3象限、第2+4象限） - 采样周期内记录的按象限划分的最大功率（第1+3象限、第2+4象限） - 采样周期内记录的平均功率因数 - 采样周期内记录的最小功率因数 - 采样周期内记录的最大功率因数 - 系统电压 - 系统最小电压 - 系统最大电压 - 相-中性线平均电压 - 相-中性线最小电压 - 相-中性线最大电压 - 四线制系统的零位移电压（平均值、最小值、最大值） 脚本说明： - SC1_PV_auto_sort.py：针对光伏发电数据，通过以原始采样率重采样修复时间戳连续性问题 - SC2_L2_auto_sort.py：针对计量侧测量数据，通过以原始采样率重采样修复时间戳连续性问题 - SC3_PV_KNN_impute.py：针对光伏发电数据，通过简单K近邻算法补全缺失数据点 - SC4_L2_KNN_impute.py：针对计量侧测量数据，通过简单K近邻算法补全缺失数据点 - SC5_Final_data_gen.py：合并光伏发电数据与计量侧测量数据，并计算用电负载功率 本数据集包含所有脚本运行后生成的结果文件（均为CSV格式）。所有经处理的变量（光伏发电量、负载功率、电网购电功率与售电功率）均以kW为单位。 更新说明： "SC1_PV_auto_sort.py"与"SC2_L2_auto_sort.py"足以完成数据清理并显现缺失数据点；"SC3_PV_KNN_impute.py"与"SC4_L2_KNN_impute.py"可有效处理短时段缺失数据，但对长时段缺失数据效果有限。本数据集仅将上述脚本作为一种数据处理方法的示例，未来将更新基于机器学习的精准预测方法以补全缺失数据。 资助机构与项目编号： - 欧盟“地平线2020”研究与创新框架计划，玛丽·居里学者资助项目（协议编号：955614） - 爱沙尼亚研究理事会资助项目（编号：PRG1086） - 爱沙尼亚能源效率卓越中心（ENER），由爱沙尼亚教育与研究部通过项目TK230资助
