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. All the notebooks that were used to can be found here.
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 to know more about the usage of the API.
If you find this repo useful for your research, please consider citing our paper:
@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.
We're currently testing a conda package. You can install in your current environment with:
conda install -c conda-forge -c nilmtk nilmtk-contrib
or create a dedicated environment (recommended) with:
conda create -n nilm -c conda-forge -c nilmtk nilmtk-contrib
Refer to this notebook for using the nilmtk-contrib algorithms, using the new NILMTK-API.
Unless you are an advanced user, prefer using the Conda package instead of the Git repostory as the latter can contain work-in-progress changes.
- NILMTK>=0.4
- scikit-learn>=0.21 (already required by NILMTK)
- Keras>=2.2.4
- 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.