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, Pytables 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

Installation

UV Support

This Python package uses uv for installation. uv is a fast and modern Python package manager that replaces tools like pip and virtualenv, with support for pyproject.toml and ultra-fast dependency resolution.

To install NILMTK, first install uv and then run:

uv pip install git+https://github.com/nilmtk/nilmtk.git

To use the deddiag dataset, install the optional dependency with:

uv pip install "nilmtk[deddiag] @ git+https://github.com/nilmtk/nilmtk

Docker Support

Docker is an open-source platform for developing, shipping, and running applications in lightweight, portable containers that bundle code, runtime, libraries, and system tools into a single package. It ensures everyone runs the same environment, regardless of host OS, and keeps NILMTK’s dependencies contained without polluting the system Python.

Build and run locally

docker build -t nilmtk-uv .
docker run --rm -it nilmtk-uv bash

Pull the pre-built image

docker pull ghcr.io/enfuego27826/nilmtk:latest
docker run --rm -it ghcr.io/enfuego27826/nilmtk:latest bash

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 Always remember to check the dates of such tutorials, many are very outdated and don't reflect NILMTK's current version or the recommended/supported setup.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 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
• 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. arXiv: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]
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. arXiv: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

Please note that NILMTK has evolved a lot since most of these papers were published! Please use the online docs as a guide to the current API.

Brief history

• August 2019: v0.4 released with the new API. See also NILMTK-Contrib.
• June 2019: v0.3.1 released on Anaconda Cloud.
• Jav 2018: Initial Python 3 support on the v0.3 branch
• Nov 2014: NILMTK wins best demo award at ACM BuildSys
• July 2014: v0.2 released
• June 2014: NILMTK presented at ACM e-Energy
• April 2014: v0.1 released

For more detail, please see our changelog.