|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Nilmtk||733||6 months ago||110||apache-2.0||Python|
|Non-Intrusive Load Monitoring Toolkit (nilmtk)|
|A node js library with a distributed leader/follower algorithm ready to be used|
|Nilm Eval||61||8 years ago||11||gpl-2.0||Matlab|
|NILM-EVAL: An evaluation framework for non-intrusive load monitoring algorithms|
|Coded||28||2 years ago||2||mit||Python|
|Continuous Degradation Detection on the Google Earth Engine|
|Oslab||21||a month ago||C|
|Repository for the lectures taught in the course named "Operating Systems Lab" at the University of Guilan, Department of Computer Engineering.|
|Saltstack Fim Module||8||8 years ago||5||bsd-3-clause||Python|
|File Integrity Monitoring (FIM) SaltStack Module|
|Omi||7||9 years ago||gpl-3.0||C|
|Gridding algorithm for Ozone Monitoring Instrument|
|Meed An Unsupervised Multi Environment Eventdetector For Non Intrusive Load Monitoring||6||3 years ago||mit||Jupyter Notebook|
|MEED: An Unsupervised Multi-Environment Event-Detector for Non-Intrusive Load Monitoring|
|Eeris_nilm||5||3 years ago||1||apache-2.0||Python|
|Non-intrusive load monitoring algorithms for project eeRIS|
|Commit Nilm||5||a year ago||22||gpl-3.0||Jupyter Notebook|
|COMMIT-NILM: COMputational MonItoring Tool for NILM Algorithms|
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.
If you are a new user, read the install instructions here. 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 Always remember to check the dates of such tutorials, many are very outdated and don't reflect NILMTK's current version or the recommended/supported setup.
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.
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:
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):
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.
For more detail, please see our changelog.