https://github.com/nilmtk/buildsys2019-paper-notebooks
https://github.com/nilmtk/buildsys2019-paper-notebooks
Last synced: 12 months ago
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- Host: GitHub
- URL: https://github.com/nilmtk/buildsys2019-paper-notebooks
- Owner: nilmtk
- Created: 2019-09-06T10:01:17.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2019-12-12T19:16:22.000Z (over 6 years ago)
- Last Synced: 2025-03-27T17:46:09.324Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 3.96 MB
- Stars: 22
- Watchers: 3
- Forks: 12
- Open Issues: 0
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Metadata Files:
- Readme: readme.md
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README
**Many changes have been done to the NILMTK-API. The code needs to be changed slightly in order to make it run with the API. Refer to the NILMTK-contrib about the latest documentation.**
# Buildsys 2019 Paper Notebooks!
In this repository you can find the notebooks that are associated with the paper results of the [NILMTK's Buildsys 2019 paper]([https://nipunbatra.github.io/papers/batra_buildsys_19.pdf](https://nipunbatra.github.io/papers/batra_buildsys_19.pdf)). The notebooks demonstrate the power of the new API.
# Experiments
The algorithms used in the paper are as follows
- Mean Algorithm
- Hart's Algorithm
- Combinatorial Optimization
- Exact FHMM
- Discriminative Sparse Coding
- Additive FHMM
- Additive FHMM with SAC (Signal Aggregate Constraints)
- Denoising Auto Encoder
- RNN
- WindowGRU
- Seq2Point
- Seq2Seq
# Notebooks
Algorithms such as AFHMM, AFHMM with SAC and Discriminative Sparse Coding are CPU intensive. All the neural networks are GPU intensive, so the a single experiment had to be run of different types of machines. All the CPU intensive algorithms were run on very powerful CPU system and every other algorithm was run on a system with a GPU. So, for every experiment we have two different notebooks - one for CPU algorithms and another for everything else.