say up front

This article mainly introduces the current public data sets, tools and others used in the field of non-intrusive load identification. If you need to read the paper and specific code implementation, see my previous article.

In addition, I have not used all the data sets, I have only used UK-DALE, so I don’t know how to deal with other data sets! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

But if your hands-on ability is not enough, you can consider:

1. Use the nilmtk toolkit directly

2. Refer to my other article NILM non-intrusive load recognition (papers with code, data) papers with codes - (papers and implementation codes) the most complete in the whole network https://blog.csdn.net/aa2962985/article/details/128635658?spm=1001.2014.3001.5501 to find the part of the public code used for data preprocessing, for example If you need to use the UK-DALE dataset, you can find papers using the UK-DALE dataset and see how they do data preprocessing .

I'll mark a few of the most common ones below

Updated: May 9, 2023 22:36:45

Public datasets:

1.REDD（The Reference Energy Disaggregation Data Set）（常用）

http://redd.csail.mit.edu/

id:redd

password:disaggregatetheenergy

2. AMPds (commonly used)

AMPds (The Almanac of Minutely Power Dataset).

The converter that comes with nilmtk corresponds to the AMPds R2013 version

Besides that

AMPds2: The Almanac of Minutely Power dataset (Version 2)

3.CER_Electricity_Data

ISSDA | Commission for Energy Regulation (CER)

4.Umass Smart Data Set

Smart - UMass Trace Repository

5. REFIT (commonly used)

The finest granularity, 8s level

REFIT: Electrical Load Measurements — University of Strathclyde

no missing point

9. UK-DALE (commonly used)

UK Domestic Appliance-Level Electricity (UK-DALE) dataset | Jack Kelly

https://data.ukedc.rl.ac.uk/browse/edc/efficiency/residential/EnergyConsumption/Domestic

There are three versions

10.ECO data set

DSG - Research Project: ECO data set

11.HES（Household Electricity Study）

Science Search

12.The tracebase data set

GitHub - areinhardt/tracebase: The tracebase appliance-level power consumption data set

13.ENERTALK

https://www.nature.com/articles/s41597-019-0212-5

Preprocessing and visualization code: GitHub - ch-shin/ENERTALK-dataset: The ENERTALK Dataset, 15 Hz Electricity Consumption Data from 22 Houses in Korea

14.BLUED

BLUED Dataset for Non-Intrusive Load Decomposition_Alex Ching Ho's Blog-CSDN Blog_blued Dataset

15. TWELVE

DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany

16.PLAID (commonly used)

PLAID2018: PLAID 2018

PLAID 2017: PLAID 2017

PLAID 2014: PLAID 2014

17.MORED: A Moroccan Buildings’ Electricity Consumption Dataset

https://github.com/MOREDataset/MORED

paper:https://www.mdpi.com/1996-1073/13/24/6737

18.Residential Power Traces for Five Houses: the iHomeLab RAPT Dataset

paper: Data | Free Full-Text | Residential Power Traces for Five Houses: The iHomeLab RAPT Dataset

pre-process code: https://github.com/ihomelab/RAPT-dataset

dataset: Residential Power Traces for Five Houses: the iHomeLab RAPT Dataset | Zenodo

19.FIRED: A Fully-labeled hIgh-fRequency Electricity Disaggregation Dataset

paper: FIRED | Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

code: GitHub - voelkerb/FIRED_dataset_helper: Files to load and use the Fully-labeled hIgh-fRequencyElectricity Disaggregation (FIRED) dataset. Files to generate statistics and plots.

20.RAE:The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis

pdf: Data | Free Full-Text | RAE: The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis

GitHub - smakonin/RAE.dataset: Scripts of the the Rainforest Automation Energy Dataset (RAE dataset)

21.COOLL:Controlled On/Off Loads Library, a Public Dataset of High-Sampled Electrical Signals for Appliance Identification

Link None.

22.IAWE:Indian Dataset for Ambient Water and Energy

iAWE

Synthetic dataset:

As the name suggests, the power data here is artificially synthesized, which is different from the data collected by the electric meter above. It is generally used as an enhanced data set.

1.SHED

A Simulated High-frequency Energy Disaggregation dataset for commercial buildings

SHED Dataset

2.SynD（A Synthetic Energy Consumption Dataset for NILM）

code: GitHub - klemenjak/SynD: A Synthetic Energy Consumption Dataset for Non-Intrusive Load Monitoring

pdf: A synthetic energy dataset for non-intrusive load monitoring in households | Scientific Data

3. SmartSim

A Device Accurate Smart Home Simulator for Energy Analytics

GitHub - sustainablecomputinglab/smartsim

4.Device-Free User Activity Detection using Non-Intrusive Load Monitoring: A Case Study

pdf: https://www.areinhardt.de/publications/2020/Reinhardt_DFHS_2020.pdf

code: GitHub - klemenjak/antgen: The AMBAL-based NILM Trace generator (for NILMTK)

How does Load Disaggregation Performance Depend on Data Characteristics? Insights from a Benchmarking Study. (2020). PDF: https://www.areinhardt.de/publications/2020/Reinhardt_eEnergy_2020.pdf

Tools (frameworks, dataset conversion tools, etc.):

NILM-TK is an open source toolkit for non-intrusive load monitoring, specifically designed to compare energy decomposition algorithms in a reproducible manner. It is a toolkit toolkit made by Jack Kelly and others.

Paper address: NILMTK | Proceedings of the 5th international conference on Future energy systems

NILMTK:

Code: GitHub - nilmtk/nilmtk: Non-Intrusive Load Monitoring Toolkit (nilmtk)

Documentation: NILMTK Documentation

all the state-of-the-art algorithms for the task of energy disaggregation

GitHub - nilmtk/nilmtk-contrib

An evaluation framework for non-intrusive load monitoring algorithms

https://github.com/beckel/nilm-eval

Related papers on NILM evaluation indicators

《On Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load Monitoring》

《Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation,Artificial Intelligence Review》

Feature Selection Related Papers in NILM

《Comprehensive feature selection for appliance classification in NILM》

DOI：10.1016/j.enbuild.2017.06.042

code:https://github.com/18D070001/Electrical-Devices-Identification-Model

Some extended applications of NILM:

Monitoring the Elderly Living Alone:

《Assessing Human Activity in Elderly People Using Non-Intrusive Load Monitoring》

DOI：10.3390/s17020351

Sustainable Homecare Monitoring System by Sensing Electricity Data | IEEE Journals & Magazine | IEEE Xplore

Use NILM to realize the recognition of home activities:

Sampling frequency corresponds to different harmonic characteristics

Non-Intrusive Load Monitoring and Classification of Activities of Daily Living Using Residential Smart Meter Data | IEEE Journals & Magazine | IEEE Xplore

Some websites about NILM (workshop, association, etc.)

The International Workshop on Non-Intrusive Load Monitoring (NILM)

http://www.nilm.eu/

http://wiki.nilm.eu/

NILM non-intrusive load identification (papers with code, data) papers with codes - (public data sets, tools, and performance indicators) The most complete network

say up front

Public datasets: