https://github.com/nilmtk/nilmtk-contrib
https://github.com/nilmtk/nilmtk-contrib
Last synced: 11 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/nilmtk/nilmtk-contrib
- Owner: nilmtk
- License: apache-2.0
- Created: 2019-06-09T08:30:01.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2021-11-25T20:37:17.000Z (over 4 years ago)
- Last Synced: 2024-06-11T17:54:14.250Z (about 2 years ago)
- Language: Python
- Size: 83.5 MB
- Stars: 115
- Watchers: 10
- Forks: 58
- Open Issues: 39
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# NILMTK-Contrib
(Note - This package only works on Python versions <= 3.11)
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://doi.org/10.1145/3360322.3360844). 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.
## Citation
If you find this repo useful for your research, please consider citing our paper:
```bibtex
@inproceedings{10.1145/3360322.3360844,
author = {Batra, Nipun and Kukunuri, Rithwik and Pandey, Ayush and Malakar, Raktim and Kumar, Rajat and Krystalakos, Odysseas and Zhong, Mingjun and Meira, Paulo and Parson, Oliver},
title = {Towards Reproducible State-of-the-Art Energy Disaggregation},
year = {2019},
isbn = {9781450370059},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3360322.3360844},
doi = {10.1145/3360322.3360844},
booktitle = {Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},
pages = {193–202},
numpages = {10},
keywords = {smart meters, energy disaggregation, non-intrusive load monitoring},
location = {New York, NY, USA},
series = {BuildSys '19}
}
}
```
For any enquiries, please contact the main authors.
## Installation Details
## 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_contrib, first install [uv](https://docs.astral.sh/uv/getting-started/installation/) and then run:
```
uv pip install git+https://github.com/nilmtk/nilmtk-contrib.git
```
## 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-contrib’s dependencies contained without polluting the system Python.
Build and run locally
```
docker build -t nilmtk-contrib .
docker run --rm -it nilmtk-contrib bash
```
Pull the pre-built image
```
docker pull ghcr.io/enfuego27826/nilmtk-contrib:latest
docker run --rm -it ghcr.io/enfuego27826/nilmtk-contrib:latest bash
```
Refer to this [notebook](https://github.com/nilmtk/nilmtk-contrib/tree/master/sample_notebooks) for using the nilmtk-contrib algorithms, using the new NILMTK-API.
## Dependencies
- NILMTK>=0.4
- scikit-learn>=0.21 (already required by NILMTK)
- Tensorflow >= 2.12.0 < 2.16.0
- cvxpy>=1.0.0
**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.**