This project is to improve performance of the existing disaggregation systems. It is composed of the design phase for obtaining the optimized base model, and the build phase for constructing the applied model. The study is composed of several experiments.
- on Algorithm Selection
- on Sampling Period Decision
- on Feature Extraction
- on Pre-verification
- on Pre-training and Result Processing
[REF] https://github.com/nilmtk/nilmtk
- conda create -n nilm -c conda-forge -c nilmtk nilmtk-contrib
- conda activate nilm
- (nilm) python -m ipykernel install --user –name nilm
- (nilm) pip install h5py==2.10.0
- (nilm) pip install tensorflow
- (nilm) conda install graphviz
- (nilm) pip install graphviz
- (nilm) pip install pydot
- (nilm) pip install tslearn
- from keras.utils import plot_model --> from keras.utils.vis_utils import plot_model
The following eight machine learning algorithms are evaluated for disaggregation performance comparison.
- Recurrent Neural Net (RNN)
- Gated Recurrent Unit (GRU)
- Window GRU (WGRU)
- Denosing Autoencoder (DAE)
- Sequence to Point (S2P)
- Residual Networks (ResNet)
- Random Forest (RF)
- Gradient Boost Machine (GBM)
To examine the effect of the sampling period of data on disaggregation performance, experiments are conducted with different sample period data.
Disaggregation performance is evaluated when models are trained with extracted features. In the previous experiment, only one input feature was trained.
Whether the developed disaggregation model can be built as a common pre-trained model showing the similarity between electricity consumption is verified.
A common pre-trained model for all households is built. Finally, disaggregation model results cannot be used directly, so they must be post-processed. For this, an intelligent reasoning method to find actual activiations is proposed.