DOI: 10.48726/6hayh-x0h25
title: PV Generation and Consumption Dataset of an Estonian Residential Dwelling

description: 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.