说在最前面
这篇文章主要介绍用于非侵入式负荷识别领域目前的公开数据集、工具和其它等，如果需要看论文及具体代码实现，看我上一篇的文章。
其外，不是所有数据集我都用过，我只用过UK-DALE,所以其它数据集你问我怎么处理我也不知道！！！！！！！！！！！！！！！！
但是如果你动手能力不足的话，可以考虑：
1.直接使用nilmtk工具包
2.参考我的另外一篇文章NILM非侵入式负荷识别（papers with code、data）带代码的论文整理——(论文及实现代码篇) 全网最全
https://blog.csdn.net/aa2962985/article/details/128635658?spm=1001.2014.3001.5501 找到里面公开代码中用来数据预处理的那部分，譬如你需要用UK-DALE数据集，你就找用UK-DALE数据集的论文，看看人家是怎么做数据预处理的。
我会在下面标注几个最常用常见的
更新时间：2023年5月9日 22:36:45
公开数据集：
1.REDD（The Reference Energy Disaggregation Data Set） （常用）
id:redd
password:disaggregatetheenergy
2.AMPds （常用）
AMPds (The Almanac of Minutely Power Dataset).
nilmtk自带的converter是对应 AMPds R2013版本
除此之外还有
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 （常用）
颗粒度最细,8s级别
REFIT: Electrical Load Measurements — University of Strathclyde
6.ENLITEN
7.GREEND
GREEND download | SourceForge.net
8.ElectricityLoadDiagrams
OEDI: Commercial and Residential Hourly Load Profiles for all TMY3 Locations in the United States
没有missing point
9.UK-DALE （常用）
UK Domestic Appliance-Level Electricity (UK-DALE) dataset | Jack Kelly
https://data.ukedc.rl.ac.uk/browse/edc/efficiency/residential/EnergyConsumption/Domestic
有三个版本
10.ECO data set
DSG – Research Project: ECO data set
11.HES（Household Electricity Study）
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
14.BLUED
非侵入式负荷分解之BLUED数据集_Alex Ching Ho的博客-CSDN博客_blued数据集
15.DEDDIAG
DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany
16.PLAID （常用）
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
20.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
链接无。
22.IAWE:Indian Dataset for Ambient Water and Energy
合成数据集：
顾名思义，这里面的电力数据是人工合成的，跟上面用电表仪器采集的数据不一样，这里一般用来做增强数据集。
1.SHED
A Simulated High-frequency Energy Disaggregation dataset for commercial buildings
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
工具（框架、数据集转换工具等）：
NILM-TK是一个非侵入式负载监测的开源工具包，专门设计用于以可再现的方式比较能量分解算法，就是Jack Kelly他们几个做的一个toolkit工具包。
论文地址： 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
NILM评价指标的相关论文
《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》
NILM中的特征选择相关论文
《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
NILM一些拓展应用：
监测独居老人：
《Assessing Human Activity in Elderly People Using Non-Intrusive Load Monitoring》
DOI：10.3390/s17020351
用NILM实现居家活动的识别：
采样频率对应不同的谐波特征
关于NILM的一些网站（workshop、协会之类的）
The International Workshop on Non-Intrusive Load Monitoring (NILM)
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