Thomas Kriechbaumer & Hans-Arno Jacobsen of The Technical University of Munich (TUM) recently released the BLOND data set, which contains voltage and current readings for aggregated circuits and matching fully-labeled ground truth data (individual appliance measurements). The study covers 53 appliances (16 classes) in a 3-phase power grid in Germany. The authors have released two versions of the data set: 1) BLOND-50 contains 213 days of measurements sampled at 50 kHz (aggregate) and 6.4 kHz (individual appliances), 2)BLOND-250 consists of the same setup: 50 days, 250 kHz (aggregate), 50 kHz (individual appliances). The data set is also described in more detail in the Scientific Data paper.
The BLUED data set contains high-frequency (12 kHz) household-level data from a single US household over a period of approximately 8 days. The data set also contains an event list of each time an appliance within the household changes state (e.g. microwave turns on). This data set was collected primarily for the evaluation of event based NIALM methods. The authors have also password protected access to the data set to keep track of its usage.
The COOLL dataset was released by researchers at the PRISME laboratory at the University of Orléans, which contains high-frequency from 12 different types of appliances. Similar to the tracebase and PLAID datasets, multiple instances of the each type were measured, and each instance was measured throughout 20 operations. During each controlled operation, current and voltage data was collected at a sample rate of 100 kHz. The dataset is summarised in an academic paper, and can be downloaded from github after filling in a registration form.
Dataport database (formerly WikiEnergy)
Delft University of Technology (TUDelft) have released DRED dataset, which contains both house level and appliance energy consumption information. The live deployment consists of several sensors measuring electricity, occupants occupancy and ambient parameters in a household. The DRED dataset includes electricity data (aggregated energy consumption and appliance level energy consumption), ambient information (room-level indoor temperature, outdoor temperature, environmental parameters), occupancy information (room-level location information of occupants, WiFi and BT RSSI information for localization) and household information (house layout, number of appliance monitored, appliance-location mapping etc). The dataset is publicly available and can be obtained from the DRED website.
In 2012, the UK Energy Savings Trust, Department of Energy and Climate Change, and Department for Environment, Food and Rural Affairs published a 15 page report called Powering the Nation. This report summarises the full 600 page Household Electricity Use Study, which aimed to better understand how electricity is consumed in UK households. As part of this study, 251 owner-occupier households were monitored across England between April 2010 and April 2011. Of these households, 26 were monitored for 12 months, and 225 were monitored for 1 month. For each household, the energy consumption of 13-51 appliances was monitored at 2 minute intervals. A software portal is currently under development to provide access to the data set, although in the meantime the data can individually requested from ICF International by contacting firstname.lastname@example.org and providing a postal mailing address and operating system details.
Released by researchers at the University of Edinburgh, the IDEAL Household Energy Dataset comprises data from 255 UK homes. Alongside electric and gas data from each home the corpus contains individual room temperature and humidity readings and temperature readings from the boiler. For 39 of the 255 homes more detailed data is available, including individual electrical appliance use data, and data on individual radiators. Sensor data is augmented by anonymised survey data and metadata including occupant demographics, self-reported energy awareness and attitudes, and building, room and appliance characteristics.
The Indraprastha Institute of Information Technology recently released the iAWE data set, which contains aggregate and sub-metered electricity and gas data from 33 household sensors at 1 second resolution. The data set covers 73 days of a single house in Delhi, India. Each individual channel of the data can be downloaded separately in either SQL or CSV format from the download section at the bottom of the webpage.
EDF Energy released a data set in 2012 containing energy measurements made at a single household in France for a duration of 4 years. Average measurements are available at 1 minute resolution of the household aggregate active power, reactive power, voltage and current, as well as the active power of 3 sub-metered circuits. Although each circuit contains a few appliances, this is the largest data set in terms of duration of measurement. The complete data set is openly available from the UCI Machine Learning Repository.
Pecan Street Research Institute (no longer available)
Pecan Street Research Institute announced the release of a new data set designed specifically to enable the evaluation of electricity disaggregation technology. A free sample data set is available to members of its research consortium, which has now been opened up to university researchers. The sample data set contains 7 days of data from 10 houses in Austin, TX, USA, for which both aggregate and circuit data is also available containing power readings at 1 minute intervals. In addition to common household loads, 2 of the houses also have photovoltaic systems and 1 house also has an electric vehicle.
REDD contains both household-level and circuit-level data from 6 US households, over various durations (between a few weeks and a few months). Each house has two-phase mains input, and 10-25 individually monitored circuits. High-frequency (kHz) current and voltage data are available for both mains circuits, while low-frequency power measurements (3-4 second intervals) are available for the appliance circuits. This data set was collected primarily for the evaluation of non-event based NIALM methods. The authors have password protected access to the data set to keep track of its usage.
The REFIT data set was released as part of the Smart Home and Energy Demand Reduction project, by David Murray and Lina Stankovic at the University of Strathclyde. The data set contains active power measurements of the aggregate and 9 individual appliances from 20 homes in the Loughborough area of the UK, at a resolution of 1 sample every 8 seconds. This makes the REFIT the only UK data set which contains appliance level data at a sample rate great than once per minute. In addition, aggregate gas consumption data was also recorded at 30 minute intervals. However, it should be noted that the data was compressed by removing samples for which the power demand had not changed since the last reading. Further details can be found in a presentation from the EEDAL 2015 conference, a detailed technical report, and the dataset readme file. In addition, a NILMTK converter is also available for the data set.
Smart* Home Data Set (via the UMassTraceRepository)
Although not collected specifically for energy disaggregation, the Smart* (Smart Star) data set provides power data from 3 thoroughly sub-metered real households. The granularity of data collected for circuit level monitors (premises aggregate and individual circuits) is one reading per second, while individual plug loads are measured roughly every few seconds. Each house contains 21-26 circuit meters and almost all appliances are measured using plug meters. At the moment, aggregate, circuit and appliance data is available for house A, but only aggregate data is available for houses B and C.
The tracebase repository contains individual appliance data with the intention of creating a database for training NIALM algorithms. The repository contains a total of 1883 days of power readings, taken at 1 second intervals, for 158 appliance instances, of 43 different appliance types. Since the aim is to create an appliance database, no aggregate measurements are collected. The data is introduced in Reinhardt et al. 2012 and is available from the tracebase repository. The files are password protected, but a password can be requested via the download page.
As always, please leave a comment if you have released your own data set or know of someone who has. Also, if you notice any errors or updates please let me know. I'll do my best to keep this list up to date!