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https://github.com/nilmtk/nilmtk

Non-Intrusive Load Monitoring Toolkit (nilmtk)
https://github.com/nilmtk/nilmtk

algorithms disaggregation energy energy-disaggregation forecasting ipython-notebook nilm nilm-algorithms nilmtk python

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Non-Intrusive Load Monitoring Toolkit (nilmtk)

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# NILMTK: Non-Intrusive Load Monitoring Toolkit



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.



NILMTK is a toolkit designed to help **researchers** evaluate the accuracy of NILM algorithms. If you are a new Python user, it is recommended to educate yourself on [Pandas](https://pandas.pydata.org/), [Pytables](http://www.pytables.org/) and other tools from the Python ecosystem.



**⚠️It may take time for the NILMTK authors to get back to you regarding queries/issues. However, you are more than welcome to propose changes, support!** Remember to check existing issue tickets, especially the open ones.



# Documentation



[NILMTK Documentation](https://github.com/nilmtk/nilmtk/tree/master/docs/manual)



If you are a new user, read the [install instructions here](https://github.com/nilmtk/nilmtk/blob/master/docs/manual/user_guide/install_user.md). It came to our attention that some users follow third-party tutorials to install NILMTK. Always remember to check the dates of such tutorials, many are very outdated and don't reflect NILMTK's current version or the recommended/supported setup.



# Why a toolkit for NILM?



We quote our [NILMTK paper](http://arxiv.org/pdf/1404.3878v1.pdf)

explaining the need for a NILM toolkit:



  > Empirically comparing disaggregation algorithms is currently

  > virtually impossible. This is due to the different data sets used,

  > the lack of reference implementations of these algorithms and the

  > variety of accuracy metrics employed.



# What NILMTK provides



To address this challenge, we present the Non-intrusive Load Monitoring

Toolkit (NILMTK); an open source toolkit designed specifically to enable

the comparison of energy disaggregation algorithms in a reproducible

manner. This work is the first research to compare multiple

disaggregation approaches across multiple publicly available data sets.

NILMTK includes:



-  parsers for a range of existing data sets (8 and counting)

-  a collection of preprocessing algorithms

-  a set of statistics for describing data sets

-  a number of [reference benchmark disaggregation algorithms](https://github.com/nilmtk/nilmtk/wiki/NILM-Algorithms)

-  a common set of accuracy metrics

-  and much more!



# Publications



If you use NILMTK in academic work then please consider citing our papers. Here are some of the publications (contributors, please update this as required):



1. Nipun Batra, Jack Kelly, Oliver Parson, Haimonti Dutta, William Knottenbelt, Alex Rogers, Amarjeet Singh, Mani Srivastava. NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring. In: 5th International Conference on Future Energy Systems (ACM e-Energy), Cambridge, UK. 2014. DOI:[10.1145/2602044.2602051](http://dx.doi.org/10.1145/2602044.2602051). arXiv:[1404.3878](http://arxiv.org/abs/1404.3878).

2. Nipun Batra, Jack Kelly, Oliver Parson, Haimonti Dutta, William Knottenbelt, Alex Rogers, Amarjeet Singh, Mani Srivastava. NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring". In: NILM Workshop, Austin, US. 2014 \[[pdf](http://nilmworkshop14.files.wordpress.com/2014/05/batra_nilmtk.pdf)\]

3. Jack Kelly, Nipun Batra, Oliver Parson, Haimonti Dutta, William Knottenbelt, Alex Rogers, Amarjeet Singh, Mani Srivastava. Demo Abstract: 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, 2014. DOI:[10.1145/2674061.2675024](http://dx.doi.org/10.1145/2674061.2675024). arXiv:[1409.5908](http://arxiv.org/abs/1409.5908).

4. Nipun Batra, Rithwik Kukunuri, Ayush Pandey, Raktim Malakar, Rajat Kumar, Odysseas Krystalakos, Mingjun Zhong, Paulo Meira, and Oliver Parson. 2019. Towards reproducible state-of-the-art energy disaggregation. In Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys '19). Association for Computing Machinery, New York, NY, USA, 193–202. DOI:[10.1145/3360322.3360844](https://doi.org/10.1145/3360322.3360844)



Please note that NILMTK has evolved *a lot* since most of these papers were published! Please use the [online docs](https://github.com/nilmtk/nilmtk/tree/master/docs/manual)

as a guide to the current API. 



# Brief history



* August 2019: v0.4 released with the new API. See also [NILMTK-Contrib](https://github.com/nilmtk/nilmtk-contrib).

* June 2019: v0.3.1 released on [Anaconda Cloud](https://anaconda.org/nilmtk/nilmtk/).

* Jav 2018: Initial Python 3 support on the v0.3 branch

* Nov 2014: NILMTK wins best demo award at [ACM BuildSys](http://www.buildsys.org/2014/)

* July 2014: v0.2 released

* June 2014: NILMTK presented at [ACM e-Energy](http://conferences.sigcomm.org/eenergy/2014/)

* April 2014: v0.1 released



For more detail, please see our [changelog](https://github.com/nilmtk/nilmtk/blob/master/docs/manual/development_guide/changelog.md).