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.
We're currently testing a conda package. You can install in your current environment with:
在当前环境安装
conda install -c conda-forge -c nilmtk nilmtk-contrib
or create a dedicated environment (recommended) with:
推荐创建新的单独环境
conda create -n nilmtk-contrib -c conda-forge -c nilmtk nilmtk-contrib
Refer to this notebook for using the nilmtk-contrib algorithms, using the new NILMTK-API.
- NILMTK>=0.4
- scikit-learn>=0.21 (already required by NILMTK)，实际测试需要0.21.3才能运行DSC算法
- 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.