Monday, 21 September 2015

Energy disaggregation for health monitoring

José and I have been working on a project to apply NILM methods to the health monitoring domain, specifically to help monitor the activity of elderly people living independently in their own homes. The approach we pursued aims to disaggregate the kettle reliably from smart meter data, without requiring any training in each home. We chose to use the kettle as the appliance of interest as it is an appliance common to almost all UK homes, is used regularly as part of most elderly people’s daily routine, and also has a signature which varies little between houses.

The core novelty of the our work is that deviations in kettle usage from the normal routine can be recognised from the disaggregated smart meter data, allowing interventions in households to occur as soon as possible. Crucially, such routines are learned individually for each household, rather than using a static routine for all households. As such, kettle usage is used as a proxy for health, since no additional sensors were installed to directly measure health parameters (e.g. heart rate).

An example of a routine for a single household is shown below. The top graph shows the probability that the kettle would be used at least once within a half hour interval, while the bottom graph shows the cumulative probability that the kettle would have been used at least once by that time of the day. It can be seen that for this household, a highly repeatable routine is followed, where the kettle is used by midday on about 80% of days.

Full details can be found in our paper to be presented at BuildSys later this year:

José Alcalá, Oliver Parson, Alex Rogers. Detecting Anomalies in Activities of Daily Living of Elderly Residents via Energy Disaggregation and Cox Processes. In: 2nd ACM International Conference on Embedded Systems For Energy-Efficient Built Environments (BuildSys), Seoul, South Korea. 2015.

Tuesday, 8 September 2015

Dataport data released in NILMTK format

The Dataport database is the world's largest source of disaggregated customer energy data. The database contains electricity data collected from 722 houses in the US; 631 in Texas, 49 in Colorado and 42 in California. The houses monitored include 501 single-family homes, 183 apartments, 35 town homes and 3 mobile homes. Access to the portal is free for members of universities, while commercial access is limited to members of Pecan Street's Industry Advisory Council.

To date, the Dataport data has been available via direct access to the database. While this provides an efficient means of querying a small amount of data, large amounts of data can take a long amount of time to download since the data is transferred in an uncompressed format. Furthermore, as with most other data sets, the data set is described in a custom format, requiring researchers to parse the data and metadata before making use of the data set.

For these reasons, we've been working with Pecan Street Inc to release a subset of the Dataport database in NILMTK HDF5 format. The HDF5 file is 1.09 GB in size, and contains one month of data from 669 of the Dataport houses, which were selected as they contain at least 8 meters. In each house, the circuit name has been converted from the Dataport names to the NILM Metadata controlled vocabulary. The produced dataset can be easily analysed using tools described in the NILMTK documentation. The HDF5 file is available via the Dataport portal under the same access control as the Dataport database.

Below is a boxplot showing the proportion of energy consumed by each circuit category across all households in the HDF5 data.

The data set is described in more detail in the following paper to be presented at the GlobalSIP Smart Buildings workshop:

Oliver Parson, Grant Fisher, April Hersey, Nipun Batra, Jack Kelly, Amarjeet Singh, William Knottenbelt, Alex Rogers. Dataport and NILMTK: A Building Data Set Designed for Non-intrusive Load Monitoring. In: 1st International Symposium on Signal Processing Applications in Smart Buildings at 3rd IEEE Global Conference on Signal & Information Processing, Orlando, FL, USA, 14-16 December, 2015.