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https://github.com/Ming-er/NeuralNILM_Pytorch

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Host: GitHub
URL: https://github.com/Ming-er/NeuralNILM_Pytorch
Owner: Ming-er
License: mit
Created: 2020-11-05T14:39:51.000Z (about 4 years ago)
Default Branch: main
Last Pushed: 2024-03-11T13:12:08.000Z (10 months ago)
Last Synced: 2024-08-02T16:32:03.631Z (6 months ago)
Language: Python
Size: 445 KB
Stars: 59
Watchers: 2
Forks: 10
Open Issues: 4
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-NILM-with-code - [Pytorch

README

# NeuralNILM_Pytorch

- **Introduction**

Given that it is difficult to reproduce some NILM algorithms, we create this code repositories implementing some state of the art (or classical) **energy disaggregation algorithms** by the means of prevailing deep learning toolkit **Pytorch** [1] and noted NILM toolkit **nilmtk** [2].

------

- **Set up**

- Create your own virtual environment with **Python > 3.6**

- Configure deep learning environment with **pytorch (GPU edition) ≥ 1.3.0 + cuDNN**

- Install other necessary dependencies, such as **Matplotlib, Scikit-learn** etc.

- Clone this repository (Please notice that **code in the folder `\nilmtk\.` is slightly different from the code in [2], so please clone this folder too**)

The environments we used are listed in the file `environment.yml`. If you use `conda`, you can use `conda env create -f environment.yml` to set up the environment.

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- **File Directory**

Since the original nilmtk toolkit may seem to be redundant for testing Neural NILM algorithms (Deep Learning method), we sorely use it for the generation of power data within a specific period of time. Thus you can **only focus on these files or folders**: `\nilmtk\api.py`, `\nilmtk\loss.py`, `\nilmtk\disaggregate`, `\tutorial\experiment_example.ipynb` and `\tutorial\code_example.ipynb`

The whole file directory is as follow (We omit some unimportant details)：

```
├── README.md
├── environment.yml //Environment dependencies
├── nilm_metadata
│ └── * //Some details are omitted
├── tutorial
│ ├── experiment_example.ipynb //How to carry out your own NILM experiment
│ ├── code_example.ipynb //How to write your own algorithms or metrics
├── nilmtk
│ ├── api.py //The core api to carry out NILM experiment
│ ├── loss.py //The evaluation Metrics
│ ├── diaggregate
| | ├── __init__.py
| | ├── attention_pytorch.py //Seq2Seq with Attention
| | ├── bilstm_pytorch.py //BiLSTM
| | ├── dae_pytorch.py //Denoising AutoEncoder
| | ├── disaggregator.py //Base Class
| | ├── energan_pytorch.py //EnerGAN
| | ├── seq2point_pytorch.py //Seq2Point
| | ├── attention_cnn_pytorch.py //CNN_Attention
| | ├── seq2seqcnn_pytorch.py //CNN_Seq2Seq
| | ├── bilstm_pytorch_multidim.py //Multiple input features BiLSTM
| | ├── dae_pytorch_multidim.py //Multiple input features DAE
| | ├── seq2point_pytorch_multidim.py //Multiple input features Seq2Point
| | └── sgn_pytorch.py //SGN
│ └── * //Some details are omitted
```

------

- **Algorithm Details**

In the folder `\nilmtk\disaggregate`, you may find several NILM algorithms, they are listed as follow:

- Denoising AutoEncoder [3]
- BiLSTM [3]
- Seq2Point [4]
- Seq2Seq [4]
- Seq2Seq with Attention [5]
- SGN [6]
- EnerGAN [7]
- CNN_Attention[8]

And several NILM algorithms with '_**multidim**' suffix, such as bilstm_pytorch_multidim, ae_pytorch_multidim, seq2point_pytorch_multidim. They are original algorithms with multiple input features(P or P + Q or P + S O or P + Q + S), which are **not included in nilmtk**[2]

Notice that our implementations of BiLSTM[3] and EnerGAN[7] are **slightly different from original papers**, and the experiment results have shown that our method will result in improved accuracy. To avoid confusing users, we will list our implementation details as follow:

- For BiLSTM, the input of the first fully connected layer is the concat of **all the hidden states** instead of the last hidden state which was the way Kelly used.
- For EnerGAN, we incorporate **reconstruction loss**(L1 loss) while training Generator, which is proved to be valid in most **pix2pix tasks**[9].

------

- **Tutorial**

- Refer to `\tutorial\experiment_example.ipynb` to know how to **carry out your own NILM experiment**.

- Refer to `\tutorial\code_example.ipynb` to know how to **write your own energy disaggregation algorithms** or **evaluation metrics**.

------
- **References**

[1] https://pytorch.org/

[2] https://github.com/nilmtk/nilmtk

[3] KELLY J, KNOTTENBELT W. Neural NILM: Deep neural networks applied to energy disaggregation[C]//The 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments. November 4-5, 2015, Seoul, South Korea: 55-64.

[4] ZHANG C, ZHONG M, WANG Z, et al. Sequence-to-point learning with neural networks for non-intrusive load monitoring[C]//The 32nd AAAI Conference on Artificial Intelligence. February 2–7, 2018, Louisiana, USA: 2604–2611.

[5] WANG Ke, ZHONG Haiwang, YU Nanpeng, et al. Nonintrusive Load Monitoring based on Sequence-to-sequence Model With Attention Mechanism[J]. Proceedings of the CSEE,2019,39(1):75-83.

[6] SHIN C, JOO S, YIM J. Subtask Gated Networks for Non-Intrusive Load Monitoring[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 1150–1157.

[7] KASELIMI M, VOULODIMOS A, PROTOPAPADAKIS E, et al. EnerGAN: A GENERATIVE ADVERSARIAL NETWORK FOR ENERGY DISAGGREGATION[C/OL]//ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona, Spain: IEEE, 2020: 1578–1582.

[8] XU Xiaohui, ZHAO Shutao, CUI Kebin. Non-intrusive Load Disaggregate Algorithm Based on Convolutional Block Attention Module[J/0L]. Power System Technology, 2021: 1-8.

[9] ISOLA P, ZHU JY, ZHOU T, et al. Image-to-Image Translation with Conditional Adversarial Networks[C/OL]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI: IEEE, 2017: 5967–5976.

------

- **Future Work To Do**

Override some of the functions which are repeated in the current framework with sealed APIs.