DOI: 10.5281/zenodo.17081452
title: LARCO - Household Laundry Appliance Resource Consumption and Operation Dataset

description: LARCO: Household Laundry Appliance Resource Consumption and Operation Dataset

Household laundry appliances such as washing machines and tumble dryers represent a significant share of residential energy and water consumption. While efficiency labels provide standardised performance indicators, the actual use of resources depends heavily on user behaviour, laundry load, programme selection, and ambient conditions. These aspects are often underrepresented in existing datasets.

The LARCO dataset addresses this gap by providing a comprehensive, open-access collection of multivariate time-series measurements of washing machines and dryers, recorded under both laboratory-controlled conditions and real-world household usage.

The dataset was created following strategic and systematic data collection protocols designed to capture the performance of household laundry appliances under both controlled and real-world conditions. By combining laboratory experiments with carefully varied parameters (ambient temperature, load sizes, and cycle programmes) and long-term household monitoring, the dataset provides a structured and comprehensive view of appliance operation.

Key recorded variables include:





Energy: voltage, current, power, power factor, frequency




Water system: inlet/outlet flow, volumes, temperatures, and pressure




Appliance temperatures: drum, door, and lint trap sensors




Indoor environment: ambient temperature and humidity




Additional modalities: three-axis vibration (200 Hz), audio recordings (11 kHz, lab only, privacy-safe), laundry weights (pre/post cycle), dryer tank water volumes, and cycle metadata.



Dataset structure

The dataset is organised into three main modalities. Each observation may include up to three parallel data streams: (i) general data capturing energy, environmental, and operational measurements, (ii) vibration data from accelerometer sensors, and (iii) audio data from laboratory recordings. These are distributed as separate ZIP archives.



general.zip: Where the main sensor data of 1Hz is stored from three environments:





Laboratory: Controlled experiments at 16 °C, 25 °C, and 31 °C with systematic variation of load sizes (0 kg up to maximum capacity –1 kg) and washing/drying programmes.




Household: Real-world monitoring of 15 appliances across 10 homes (2004–2020), capturing natural variability in use.




Exploratory: Early recordings for testing and validation.




vibrations.zip: where all the accelerometer sensor files are stored, it includes two environments:



Laboratory

Household



audio.zip: where all the audio files are stored from the laboratory environment. 



! The original WAV files were compressed using FLAC. To convert them back to WAV run the following command: 

find audio -type f -name "*.flac" -exec flac -d {} \;





Each cycle includes 1 Hz general data files, optional 200 Hz accelerometer files, and 11 kHz audio files (for some laboratory appliances). Metadata files provide appliance details, sensor specifications, units, sampling frequencies, and environment information. Each zip file contains an aggregated CSV summary, and additional metadata about each experiment is also included for quick access: general_data.csv, general_data_acc.csv andgeneral_data_audio.csv. Each zip file contains metadata.xlsx file with the detailed information about sensor types and models, their descriptions and acquisition frequencies.

Applications

The dataset supports research in:





Appliance efficiency assessment and benchmarking




User behaviour analysis and modelling




Intrusive and non-intrusive load monitoring (ILM/NILM)




Personalised energy feedback and eco-feedback systems




Demand-side management and sustainable energy strategies



 

Availability

Data are provided as raw sensor time-series without post-processing, ensuring maximum flexibility for downstream use. Aggregated and metadata files facilitate navigation, filtering, and integration into analytical workflows.