Energy Disaggregation using Variational Autoencoders

This code implements the Variational Autoencoders model used in the paper :

Langevin, A., Carbonneau, M. A., Cheriet, M., & Gagnon, G. (2021). Energy Disaggregation using Variational Autoencoders. arXiv preprint arXiv:2103.12177.

Comparison methods:

Kelly, J., & Knottenbelt, W. (2015, November). Neural nilm: Deep neural networks applied to energy disaggregation. In Proceedings of the 2nd ACM international conference on embedded systems for energy-efficient built environments (pp. 55-64).

https://github.com/JackKelly/neuralnilm

Chaoyun Zhang, Mingjun Zhong, Zongzuo Wang, Nigel Goddard, and Charles Sutton. "Sequence-to-point learning with neural networks for nonintrusive load monitoring." Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), Feb. 2-7, 2018.

https://github.com/MingjunZhong/seq2point-nilm

S2SSPan, Y., Liu, K., Shen, Z., Cai, X., & Jia, Z. (2020, May). Sequence-to-subsequence learning with conditional gan for power disaggregation. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3202-3206). IEEE.

https://github.com/DLZRMR/seq2subseq

Setup

1. Create your own environment with Python > 3.6
2. Configure deep learning environment with Tensorflow
3. Install others requirement packages
4. Clone this repository

Datasets and preprocessing

1. Download UKDALE files and extract .dat files in each house folder.

Example:

Data/
|-- UKDALE/
|   |-- house_1
|   |   |-- channel1.dat
|   |   |-- channel2.dat
|   |   |-- ...
|   |-- house_2
|   |   |-- channel1.dat
|   |   |-- ...
|   |-- ...

2. Execute the preprocess code

python uk_dale_preprocess.py

It will generate these files for each house and the each appliance:

Data/
|-- UKDALE/
|   |-- Dishwasher_appliance_house_1
|   |-- Dishwasher_main_house_1
|   |-- Fridge_appliance_house_1
|   |-- Fridge_main_house_1
|   |-- ...
|   |-- Dishwasher_appliance_house_2
|   |-- Dishwasher_main_house_2
|   |-- Fridge_appliance_house_2
|   |-- Fridge_main_house_2
|   |-- ...

Training and testing

The training is performed with the following command:

python NILM_disaggregation.py --gpu 0 --config Config/House_2/WashingMachine_VAE.json

Where --gpu is used to select a specific GPU, and --config to select the config file associated with the training to execute.

The test is performed with the following command:

python NILM_test.py --gpu 0 --config Config/House_2/WashingMachine_VAE.json

The script tests the last trained model of the selected configuration. It predicts the energy disaggregation on the test data e.g., house 2 and saves it in "pred_1.npy". It also prints the results for the metrics: MAE, ACC, PRECISION, RECALL, F1-SCORE, SAE and saves the scores in "results_median.npy".

Example:

Best Epoch : 82
6.366289849142183 # MAE
0.8244607666324364 # ACC
0.8333902355752817 # PREC
0.9463532832566028 # RECALL
0.8862867905689065 # F1-SCORE
[0.35107847] # SAE