# nilmtk（镜像）

**Repository Path**: lewous/nilmtk

## Basic Information

- **Project Name**: nilmtk（镜像）
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-13
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[![Build Status](https://travis-ci.org/nilmtk/nilmtk.svg?branch=master)](https://travis-ci.org/nilmtk/nilmtk) [![Install with conda](https://anaconda.org/nilmtk/nilmtk/badges/installer/conda.svg)](https://anaconda.org/nilmtk/nilmtk) [![conda package version](https://anaconda.org/nilmtk/nilmtk/badges/version.svg)](https://anaconda.org/nilmtk/nilmtk)

# 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. 


# Documentation

[NILMTK Documentation](https://gitee.com/lewous/nilmtk/tree/master/docs/manual/README.md) 

# Why a toolkit for NILM?

We quote our [NILMTK paper](https://gitee.com/lewous/nilmtk/tree/master/docs/paper.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://gitee.com/lewous/nilmtk/wikis/NILM-Algorithms)
-  a common set of accuracy metrics
-  and much more!

# Publications

Please see our [list of NILMTK publications](http://nilmtk.github.io/#publications).  If you use NILMTK in academic work then please consider citing our papers.

Please note that NILMTK has evolved *a lot* since these papers were published! Please use the [online docs](https://gitee.com/lewous/nilmtk/tree/master/docs/manual) as a guide to the current API.


# Keeping up to date with NILMTK

* [google mailing list](https://groups.google.com/forum/#!forum/nilmtk-announce): stay up to speed with NILMTK.  This is a low-traffic mailing list.  We'll just announce new versions, new docs etc.
* [NILMTK on Twitter](https://twitter.com/nilmtk).


# History

* April 2014: v0.1 released
* June 2014: NILMTK presented at [ACM e-Energy](http://conferences.sigcomm.org/eenergy/2014/)
* July 2014: v0.2 released
* Nov 2014: NILMTK wins best demo award at [ACM BuildSys](http://www.buildsys.org/2014/)

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