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 illustration (produced using nilmtk) of what NILM, in general, can do.

• April 2014: v0.1 released
• June 2014: NILMTK presented at ACM e-Energy
• July 2014: v0.2 released
• Nov 2014: NILMTK wins best demo award at ACM BuildSys

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

• Batra, N., Kelly, J., Parson, O., Dutta, H., Knottenbelt, W., Rogers, A., Singh, A., Srivastava, M. (2014). NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring. In Fifth International Conference on Future Energy Systems (ACM e-Energy). Cambridge, UK. arXiv:1404.3878 DOI:10.1145/2602044.2602051

Bibtex:

@inproceedings{NILMTK,
   title         = {NILMTK: An Open Source Toolkit for
                    Non-intrusive Load Monitoring},
   year          = {2014},
   author        = {Batra, Nipun and Kelly, Jack and Parson, Oliver and
                    Dutta, Haimonti and Knottenbelt, William and
                    Rogers, Alex and Singh, Amarjeet and Srivastava, Mani},
   booktitle     = {Fifth International Conference on Future Energy
                    Systems (ACM e-Energy)},
   address       = {Cambridge, UK},
   archivePrefix = {arXiv},
   arxivId       = {1404.3878},
   doi           = {10.1145/2602044.2602051},
   eprint        = {1404.3878}
}

• Kelly, J., Batra, N., Parson, O., Dutta, H., Knottenbelt, W., Rogers, A., Singh, A., Srivastava, M. (2014). NILMTK v0.2: A Non-intrusive Load Monitoring Toolkit for Large Scale Data Sets. In The first ACM Workshop On Embedded Systems For Energy-Efficient Buildings at BuildSys 2014. Memphis, USA. DOI:10.1145/2674061.2675024 arXiv:1409.5908

Bibtex:

@Inproceedings{kelly2014NILMTKv02,
  Title      = {NILMTK v0.2: A Non-intrusive Load Monitoring
                Toolkit for Large Scale Data Sets},
  Author     = {Kelly, Jack and Batra, Nipun and Parson, Oliver and
                Dutta, Haimonti and Knottenbelt, William and
                Rogers, Alex and Singh, Amarjeet and Srivastava, Mani},
  Booktitle  = {The first ACM Workshop On Embedded Systems For
                Energy-Efficient Buildings at BuildSys 2014},
  Year       = {2014},
  Doi        = {10.1145/2674061.2675024},
  Eprint     = {1409.5908},
  Eprinttype = {arXiv},
  Address    = {Memphis, USA}
}

N.B. NILMTK has evolved a lot since these papers were published! Please use the online docs as a guide to the current API.

• NILMTK-Announce mailing list: stay up to speed with NILMTK. It will be a fairly low-traffic mailing list. We'll just announce new versions, new docs etc.
• NILMTK on Twitter.

The project is in its early stages.

Please note that NILMTK is currently a research tool. It is not yet ready for use by end-users, although we certainly hope that NILMTK will be capable of doing 'plug and play' disaggregation in the future.

Please see the docs for more info.

Please fill in our NILMTK survey to help us get a better idea of who's using NILMTK and what's important to you.

Please use our github issue queue to submit bug reports, rather than emailing them, which will allow any of us to respond to your issue.

Before opening an issue:

1. Search the issue queue in case a duplicate already exists
2. Pull the latest changes from the repository master branch to see if the error goes away

If not, please open a new issue, ensuring:

1. The title summarises the issue
2. The issue is described in prose
3. A snippet of code is included which will allow us to recreate the bug
4. A copy-paste of the stack produced error
5. Include a copy-paste of the output from nilmtk.utils.show_versions()

NB: The following procedure is for Ubuntu like Linux variants (debian based). Please adapt accordingly for your OS. We would welcome installation instructions for other OS as well. We would recommend using Anaconda, which bundles togther most of the required packages. After installing Anaconda, please do the following:

• Updating anaconda

conda update --yes conda

• Installing HDF5 libaries and python-dev

sudo apt-get install libhdf5-serial-dev python-dev

• Installing git client

sudo apt-get install git

• Installing pip and other dependencies which might be missing from Anaconda

conda install --yes pip numpy scipy six scikit-learn pandas numexpr pytables dateutil matplotlib networkx

• Installing NILM Metadata.

git clone https://github.com/nilmtk/nilm_metadata/
cd nilm_metadata
python setup.py develop
cd ..

• Installing hmmlearn

git clone git://github.com/hmmlearn/hmmlearn.git
cd hmmlearn
python setup.py install
cd ..

• Misc. pip installs

pip install psycopg2 nose coveralls coverage

• Finally! Install nilmtk

git clone https://github.com/nilmtk/nilmtk.git
cd nilmtk
python setup.py develop
cd..

• Run tests

cd nilmtk
nosetests

• Install Anaconda, which bundles togther most of the required packages.

• Install git client

• Installing pip and other dependencies which might be missing from Anaconda

$ conda install --yes pip numpy scipy six scikit-learn pandas numexpr pytables dateutil matplotlib networkx

• Installing NILM Metadata from git bash

$ git clone https://github.com/nilmtk/nilm_metadata/
$ cd nilm_metadata
$ python setup.py develop
$ cd ..

• Installing hmmlearn

git clone git://github.com/hmmlearn/hmmlearn.git
cd hmmlearn
python setup.py install
cd ..

• Installing postgresql support (currently needed for WikiEnergy converter) Download release for your python environment: http://www.stickpeople.com/projects/python/win-psycopg/

• Misc. pip installs

$ pip install nose pbs coveralls coverage

• Finally! Install nilmtk from git bash

$ git clone https://github.com/nilmtk/nilmtk.git
$ cd nilmtk
$ python setup.py develop
$ cd..

• Run tests

$ cd nilmtk
$ nosetests