Here you can find the code for the two proposed Neural Network architectures from

Sliding Window Approach for Online Energy Disaggregation Using Artificial Neural Networks, O. Krystalakos, C. Nalmpantis and D. Vrakas, SETN, 2018

Full Paper: 3200947.3201011

All code is written using Keras and Tensorflow.

You can find NILMTK-compatible versions of these networks on https://github.com/OdysseasKr/neural-disaggregator.

Requires NILMTK to run. You can find it here: https://github.com/nilmtk/nilmtk.

In each folder you can find a README with instructions on how to run the experiments. For every network you will find 4 Python files:

• experiment.py: Code for running the experiment. Each experiment includes training and evaluating the network.
• gen.py: Downloads all necessary resources and generates a trainset and a testset.
• model.py: The network architecture.
• metrics.py: The metrics used to evaluate the network.

The experiments are available for the following appliances from the UKDALE dataset:

• Dishwasher
• Fridge
• Kettle
• Microwave
• Washing Machine

Neural NILM: https://arxiv.org/pdf/1507.06594.pdf
Original Sequence-to-point: https://arxiv.org/pdf/1612.09106v3.pdf.