# nilmtk-contrib（镜像）

**Repository Path**: lewous/nilmtk-contrib

## Basic Information

- **Project Name**: nilmtk-contrib（镜像）
- **Description**: The state-of-the-art algorithms for the task of energy disaggregation implemented using NILMTK's Rapid Experimentation API.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-24
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


# nilmtk-contrib

This repository contains all the state-of-the-art algorithms for the task of energy disaggregation implemented using NILMTK's Rapid Experimentation API. You can find the paper [here](https://nipunbatra.github.io/papers/batra_buildsys_19.pdf). All the notebooks that were used to can be found [here](https://github.com/nilmtk/buildsys2019-paper-notebooks).



Using the NILMTK-contrib you can use the following algorithms:
 - Additive Factorial Hidden Markov Model
 - Additive Factorial Hidden Markov Model with Signal Aggregate Constraints
 - Discriminative Sparse Coding
 - RNN
 - Denoising Auto Encoder
 - Seq2Point
 - Seq2Seq
 - WindowGRU

The above state-of-the-art algorithms, have been added to this repository. 

You can do the following using the new NILMTK's Rapid Experimentation API
 - Training and Testing across multiple appliances
 - Training and Testing across multiple datasets (Transfer learning)
 - Training and Testing across multiple buildings
 - Training and Testing with Artificial aggregate
 - Training and Testing with different sampling frequencies
 
Refer to this [notebook](https://github.com/nilmtk/nilmtk-contrib/blob/master/sample_notebooks/NILMTK%20API%20Tutorial.ipynb) to know more about the usage of the API.

# Installation Details

Currently, we are still working on developing a conda package, which might take some time to develop. In the meanwhile, you can install this by cloning the repository in the Lib/Site-packages  in your environment.  Rename the directory to **nilmtk_contrib**. Refer to this [notebook](https://github.com/nilmtk/nilmtk-contrib/tree/master/sample_notebooks) for using the nilmtk-contrib algorithms, using the NILMTK-API.

# Dependencies

Scikit-learn>=0.21
Keras>=2.2.4 
Cvxpy>=1.0.0
NILMTK-0.3


**Note: For faster computation of neural-networks, it is suggested that you install keras-gpu, since it can take advantage of GPUs. The algorithms AFHMM, AFHMM_SAC and DSC are CPU intensive, use a system with good CPU for these algorithms.**