/nilmtk

Primary LanguageJupyter NotebookApache License 2.0Apache-2.0

По наработкам

Конвертация данных ECO представлена в Test.ipynb

Эксперименты с алгоритмами в comparing_nilm_algorithms.ipynb

Далее идет описание используемой библиотеки

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

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 Always remember to check the dates of such tutorials, many are very outdated and don't reflect NILMTK's current version or the recommended/supported setup.

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!