# pica_WaveNILM（镜像）

**Repository Path**: lewous/wavenilm

## Basic Information

- **Project Name**: pica_WaveNILM（镜像）
- **Description**: a student's work published at ICASSP 2019
- **Primary Language**: Unknown
- **License**: MIT
- **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

# WaveNILM
WaveNILM implementation in Keras, with Tensorflow backend as described in: 

**A. Harell, S. Makonin and I. V. Bajić, "Wavenilm: A Causal Neural Network for Power Disaggregation from the Complex Power Signal," ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, United Kingdom, 2019, pp. 8335-8339. doi: 10.1109/ICASSP.2019.8682543**

If using this code in any research, please cite the original paper, seen above.

## Dependencies:
* Keras 2.2.2 
* Tensorflow 1.8.0 
* Sacred 0.7.4

## Installation instructions:
The code was developed using pipenv*, and the environment file is included in the repository
* Create a folder for the work environment
* Copy pipfile.lock to the folder
* Run  
> pipenv install --ignore-pipfile
* Start the work environment using 
> pipenv shell 
* Clone repository to the work environement folder
* Follow instruction in data/readme.md for copying or creating dataset files


\* To install pipenv itself run:
> pip install pipenv

## Running instructions:
Turn on environment using **pipenv shell**. then run code from src folder as follows:
> python waveNILM.py with <\optimizer name here\> \<config change values here\>'

Currently configured optimizers include 'adam' and 'SGD', to include other optimizers, or to modify the optimizer parameters, see src/waveNILM.py'. Other controlable parameters include training length in samples, number of epochs to train, a cross validation flag, and more. For a full list of the parameters see 'src/waveNILM.py'

Some example runing parameters:
To run the noisy scenario on the full AMPds2 dataset, with active power as both input and output, with cross validation and adam optimizer simply run
> python waveNILM.py with adam

To run the same scenario but only on the first year of AMPds2, run:
> python waveNILM.py with adam trn_len=525600

To run a denoised scenario on appliances number 8,14,16, with all other parameters the same, run:
> python waveNILM.py with adam app_inds=[8,14,16] noise_mode=0