Implementations of NILM disaggegregators using Neural Networks, using NILMTK and Keras.
Most of the architectures are based on [Neural NILM: Deep Neural Networks Applied to Energy Disaggregation]
The implemented models are:
- Denoising autoencoder (DAE) as mentioned in Neural NILM
- Recurrent network with LSTM neurons as mentioned in Neural NILM
- Recurrent network with GRU. A variation of the LSTM network in order to compare the two types of RNNs
- Window GRU. A variation of the GRU network in that uses a window of data as input. As described in Sliding Window Approach for Online Energy Disaggregation Using Artificial Neural Networks by Krystalakos, Nalmpantis and Vrakas
- Short Sequence to Point Network based on the architecture in original paper