# nilmtk_buildsys2019-paper-notebooks（镜像）

**Repository Path**: lewous/buildsys2019-paper-notebooks

## Basic Information

- **Project Name**: nilmtk_buildsys2019-paper-notebooks（镜像）
- **Description**: notebooks associated with the paper results of the NILMTK's Buildsys 2019 paper. 
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-24
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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.