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neuralnilm

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Deep Neural Networks Applied to Energy Disaggregation

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# neuralnilm
Deep Neural Networks Applied to Energy Disaggregation

**Please note that this code was never finished! It is incomplete and unsupported! Please do not use unless you really, really know what you are doing! Please see my Neural NILM prototype code to see exactly what I implemented in my Neural NILM paper!  Your best bet if you want to re-implement Neural NILM would probably be start from scratch using a modern framework like TensorFlow, and perhaps use nilmtk to help load the datasets.**

This is a complete re-write of the
[Neural NILM Prototype](https://github.com/JackKelly/neuralnilm_prototype).

# Getting started
[Here is a short iPython notebook to show how to load activations from UKDALE](https://github.com/JackKelly/neuralnilm/blob/master/notebooks/extract_activations.ipynb).

Please also note that my neuralnilm code is only provided as a proof
of concept and as a reference.  It isn't yet in a state where it's
easy for other people to run the code.  Unfortunately I won't have
time to work on the Neural NILM code or to provide support anytime 
in the forseable future.  So the code is provided 'as is' without 
any support at the moment.  Maybe one day I'll have time to support
the code or to update the code.

That said, if you're really determined to read and understand this
code then here's a quite description of the basic architecture:

Each 'experiment' is defined in a Python script in
`experiment_definitions/`.  Each of these files must have a `run`
function which, well, runs the experiment!

`experiment_definitions/` also should include a text file called
`job_list.txt` which is just a list of experiment names like `e575`.
The script `scripts/run_experiments.py` looks at `job_list.txt` to
find the next job to run.  After each job completes,
`run_experiments.py` removes that job from the `job_list`.  In this
way, it is possible to add, remove or re-order jobs from the
`job_list` while an experiment is running.

The re-usable code which makes up the bulk of the NeuralNILM package
lives in `neuralnilm/neuralnilm`.  `net.py` includes a `Net` class
which handles the construction of each neural network.  `trainer.py`
includes a `Trainer` class which trains each net and sends metrics to
the Mongo database.

`neuralnilm/neuralnilm/data` includes the mechanisms for loading and
transforming data.  This is designed to be very modular so you can
easily mix and match data sources, pre-processing steps and networks.
For a quick overview of how this all works, take a look at some of the
python files in the `experiment_definitions` directory.

`neuralnilm/neuralnilm/monitor` contains the mechanisms for loading
metrics and metadata from the Mongo database and plotting it.

# Requirements
See `requirements.txt` for the required Python packages.  Also requires Mongo DB.

# Configuration
You need to create a `~/.neuralnilm` text file to specify some
parameters.  Please see `example_config_file`.

## MongoDB remote access

See http://www.mkyong.com/mongodb/mongodb-allow-remote-access/

neuralnilm

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