multi-nilm | Multi-NILM : Multi Label Non Intrusive Load Monitoring

by ChristoferNal Python Version: Current License: MIT

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kandi X-RAY | multi-nilm Summary

multi-nilm is a Python library typically used in Manufacturing, Utilities, Energy, Utilities applications. multi-nilm has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

This repository is based on our paper with title: "On time series representations for multi-label NILM" [1] and it can be used to replicate the experiments. It defines a framework for multi-label NILM systems and includes the following time series representations: Signal2Vec, BOSS, SFA, WEASEL, DFT, SAX, 1d-SAX, PAA; and an implementation of delay embedding using Taken's theorem. Feel free to reuse, modify and extend this repository.

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Reuse

Support

multi-nilm has a low active ecosystem.

It has 41 star(s) with 15 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 4 have been closed. On average issues are closed in 32 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of multi-nilm is current.

Quality

multi-nilm has 0 bugs and 0 code smells.

Security

multi-nilm has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

multi-nilm code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

multi-nilm is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

multi-nilm releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

It has 2451 lines of code, 157 functions and 23 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed multi-nilm and discovered the below as its top functions. This is intended to give you an instant insight into multi-nilm implemented functionality, and help decide if they suit your requirements.

Train the model
Populate training parameters from an Environment
Sets up the TensorFlow environment
Returns the type of the TimeSeriesTransformer
Setup train data
Read all meters in the dataset
Set up one building
Normalize the columns of the dataframe
Runs the experiments
Setup the running params
Setup data for all buildings
Estimate the time series for the given time series
Creates an Environment
Setup training environment
Setup the running parameters for training
Create an Environment for a single building
Approximate the wavelet transform from a series
Given a data_per_builds data_per_builds_data_from_data_per_builds
Create an Environment for multibuilding learning learning
Runs the engine
Create an Environment for a single building learning
Calculate the approximate time delay embeddings
Approximate a series using the transformer
Compute mutual information for a series
Read a dataframe containing meter groups
Rename meter
Helper function to create a map of meter groups

Get all kandi verified functions for this library.

multi-nilm Key Features

No Key Features are available at this moment for multi-nilm.

multi-nilm Examples and Code Snippets

No Code Snippets are available at this moment for multi-nilm.

Community Discussions

No Community Discussions are available at this moment for multi-nilm.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install multi-nilm

You can download it from GitHub.
You can use multi-nilm like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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