ACS-F1

ACS-F1 dataset Database of appliance consumption signatures

Realization
Christophe Gisler
Antonio Ridi
Jean Hennebert
Contact
Jean Hennebert
jean.hennebert@hefr.ch
Content
appliance consumption signatures and categories to be used for appliance recognition tasks.
Keywords
  • Home appliances
  • Protocols
  • Monitoring
  • Accuracy 
  • Portable computers

ACS-F1 is a database of appliance consumption signatures and two test protocols to be used for appliance recognition tasks. Made in 2013, a second version is also available here.By means of plug-based low-end sensors measuring the electrical consumption at low frequency, typically every 10 seconds, we made two acquisition sessions of one hour on about 100 home appliances divided into 10 categories and 6 consumption measures. We now give free access to this ACS-F1 database. The proposed test protocols will help the scientific community to objectively compare new algorithms.

Overview

In order to acquire the various electrical signals of the target appliances, we first had to set several acquisition modalities and parameters :

  • Acquisition sampling frequency: 10-1 Hz – Most approaches use high sampling frequency in order to capture the electrical noise generated by the appliance and use this noise to distinguish the different categories of appliances. In our approach, we use a larger time space in order to build what we call an electrical consumption signature of an appliance.
  • Acquisition duration: 2 sessions of 1 hour each – According to the sampling frequency, one hour of acquisition is a reasonable value to build an electrical consumption signature for an appliance, so that all possible running states are recorded. These two acquisition sessions are performed for each appliance of each category, at a different time and with a different use profile. It is essential to have a common and varied use of the equipment being acquired.
  • Number of categories: 10 – We selected the following categories :
    • Fridges & freezers,
    • TVs (LCD),
    • Hi-Fi systems (with CD players),
    • Laptops,
    • Computer stations (with monitors),
    • Compact fluorescent lamps (CFL),
    • Microwaves,
    • Coffee machines,
    • Mobile phones (via battery charger),
    • Printers.

    Those categories well represent most common appliances in home or office

  • Number of appliance instances per category: 10.
  • Number of appliances per acquisition device (i.e. PLOGG): 1 – We want to record disaggregated signals.

We measured the consumption in terms of :

  • real power (W),
  • reactive power (var),
  • RMS current (A),
  • frequency (Hz),
  • RMS voltage (V),
  • phase of voltage relative to current (φ).

Data Format

Two different data formats are available. An XML data structure has been designed for storing the raw observations, some meta-data and the ground truth values of the appliance categories.

A MAT data structure is also available. Six different headers introduce the lists of data for every feature, i.e. :

# name: rmsVolt
# type: matrix
# rows: 1
# columns: 360

Protocols

The proposed test protocols will help to objectively compare new algorithms.

Test Protocol 1.0 – Intersession

In the test protocol 1.0 (or intersession protocol) for appliance recognition, all instances of acquisition session 1 must be taken in the train (resp. test) set. In other words, cardinalities of both train and test sets are equal. For a given recording, it is allowed to use the whole duration of the signal, namely 1 hour. Classification results must be presented in the form of a confusion matrix and the overall recognition rate.

Test Protocol 2.0 – Unseen Instances

In the test protocol 2.0 (or unseen instances protocol) for appliance recognition, all instances of both sessions are taken to perform a k-fold cross-validation, where k = 10. In other words, we randomly partitioned all instances into k = 10 subsets. The cross-validation process consists then in taking successively each of the k-folds for testing and the remaining k = 1 ones (i.e. 9) for the training. As for test protocol 1.0, for a given recording, it is allowed to use the whole duration of the signal, namely 1 hour. Classification results must be presented in the form of a confusion matrix and the overall recognition rate averaged over the k-folds.

Terms of use

The owner of the ACS-F1 database is iCoSys Institute, University of Applied Sciences HES-SO//Fribourg, Engineering and Architecture Faculty, Pérolles 80, CH-1700 Fribourg, Switzerland.

The data is supplied with no guarantee of accuracy or usability. Therefore, we can not be liable of any loss or damage resulting of the use of the database. We can not guarantee to maintain the ACS-F1 database. The ACS-F1 database is for non-commercial research only.

Reference for citation

If you use our ACS-F1 database, we kindly ask you to cite our work:

  • C. Gisler, A. Ridi, and J. Hennebert, “Appliance Consumption Signature Database and Recognition Test Protocols,” in WOSSPA2013 The 9th International Workshop on Systems, Signal Processing and their Applications 2013, 2013, pp. 336-341.
    [Website] [PDF] [Bibtex]
    @conference{gisler:2013:wosspa,
    author = "Christophe Gisler and Antonio Ridi and Jean Hennebert",
    abstract = "We report on the creation of a database of appliance consumption signatures and two test protocols to be used for appliance recognition tasks. By means of plug-based low-end sensors measuring the electrical consumption at low frequency, typically every 10 seconds, we made two acquisition sessions of one hour on about 100 home appliances divided into 10 categories: mobile phones (via chargers), coffee machines, computer stations (including monitor), fridges and freezers, Hi-Fi systems (CD players), lamp (CFL), laptops (via chargers), microwave oven, printers, and televisions (LCD or LED). We measured their consumption in terms of real power (W), reactive power (var), RMS current (A) and phase of voltage relative to current (varphi). We plan to give free access to the database for the whole scientific community. The proposed test protocols will help to objectively compare new algorithms. ",
    booktitle = "WOSSPA2013 The 9th International Workshop on Systems, Signal Processing and their Applications 2013",
    doi = "10.1109/WoSSPA.2013.6602387",
    isbn = "9781467355407",
    keywords = "electric consumption modelling, benchmark protocols",
    note = "Some of the files below are copyrighted. They are provided for your convenience, yet you may download them only if you are entitled to do so by your arrangements with the various publishers.",
    pages = "336-341",
    title = "{A}ppliance {C}onsumption {S}ignature {D}atabase and {R}ecognition {T}est {P}rotocols",
    Pdf = "http://hennebert.org/download/publications/wosspa-2013-appliance-consumption-signature-database-and-recognition-test-protocols.pdf",
    year = "2013",
    }

Download

The ACS-F1 Database is available now ! It’s free to download for non-commercial research.

Download : ACS-F1 Database