Non-Intrusive Load Monitoring (NILM) is the process of estimating the energy consumed by individual appliances given just a whole-house power meter reading. In other words, it produces an (estimated) itemised energy bill from just a single, whole-house power meter.

NILM is sometimes called:

• "non-intrusive appliance load monitoring (NALM or NIALM)"
• "[electriciy | energy | smart meter] disaggregation"

Below is an illustration¹ of what NILM, in general, can do.

http://nilmtk.github.io/nilmtk/

The project is in its early stages.

At present, NILMTK can:

• import data from the following data sets: REDD, Pecan Street, AMPds, iAWE, SMART* and UKPD
• store imported data in a common in-memory format, which can also be exported to a standard on-disk format
• compute diagnostic statistics for the raw data (uptime, dropout rate, find gaps etc)
• pre-process the data (downsample, fill gaps, mask appliance data with gaps from the mains data, select only contiguous blocks, normalise power etc)
• compute usage statistics (distribution of appliance activity per day, week or month, distribution of on-power, proportion of total energy per appliance, on-durations etc)
• provide a common input and output interface to NILM algorithms
• disaggregate data using two supervised benchmark algorithms: combinatorial optimisation and factorial hidden Markov model (these are not competitive with the current state-of-the-art. More sophisticated NILM algorithms will be added to NILMTK later this year.)
• compute a range of NILM performance metrics (confusion matrices, error in assigned energy, F1 score, fraction of energy assigned correctly etc).
• work has started on a disaggregation web interface. Here's a demo.

If you just want to use the code without modifying it then:

python setup.py install

(you may have to run as sudo)

If you want to get involved in development then:

python setup.py develop

1. Pandas
2. matplotlib
3. numpy => 1.8
4. scikit-learn > 0.13

Loading a supported dataset is simple. For example, to load REDD:

from nilmtk.dataset import REDD
redd = REDD()
redd.load('/data/REDD/low_freq/')

Please see the examples folder for further information on getting started.

We have started writing a user guide although it is by no means complete yet. A full user guide will be written in February.

We also have API documentation. This documentation covers almost all functions in NILMTK but needs some tidying up which will be done over February.

Please see the nilmtk wiki for more details.

• To make the code as easy as possible to use and to maintain, it will be undergoing some refactoring in Febrary 2014.
• Improve the documentation
• Write more unit tests
• easy installation via pip
• Build a semantic wiki for storing information relevant to NILM (e.g. a database of appliances) - NILMTK will integrate with the wiki wherever possible, especially regarding appliance metadata.

1. The image is from the following paper and since the main author is contributing to nilmtk, so no permission issues. The reference is: Nipun Batra, Haimonti Dutta, Amarjeet Singh, “INDiC: Improved Non-Intrusive load monitoring using load Division and Calibration”, to appear at the 12th International Conference on Machine Learning and Applications (ICMLA’13) will be held in Miami, Florida, USA, December 4 – December 7, 2013 Preprint. IPython notebook