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

说在最前面

    研究生三年快毕业了，毕业前整理一下该领域的研究工作。正所谓，我栽树，后人乘凉。研究NILM的时候，个人觉得最快的方法是直接复现别人的论文，或者甚至用别人论文的代码直接跑出来体会整个流程（数据集导入->数据预处理->运行模型->输出结果）。研究生三年找遍了github上的一些相关的代码收集起来，现在快要毕业了，整理一下，就当做是研究生三年的一个交待。

    个人研究NILM主要是利用深度学习、机器学习方面的方法，数学优化（遗传算法、粒子群优化）之类的研究得比较少，因此本文的分享主要**聚集于已公开的基于深度学习**来做非侵入式负荷识别的论文及相关公开的源码。

注：文中关于论文和代码的时效性为22年6月前后，后面我没有再阅读过相关论文和找过相关的公开代码了（主要是自己的论文后面投出去录用了），这方面的工作后面没有再深入了，好像就那时候开始流行用GNN来做了，因为之前普通的CNN、LSTM甚至Transformer都做过很多了，没得水论文了。此外，下面论文的代码我也只是跑通了个别几个感兴趣的，个别的你没跑通你问我我也不知道怎么弄。

公开数据集、工具和性能指标篇请看我另外一篇文章：

NILM非侵入式负荷识别（papers with code、data）带代码的论文整理——(公开数据集、工具、和性能指标篇) 全网最全

本文的适用读者：仅对相关领域有兴趣的在校的学生及学者

（鉴于最近有网友私信留言，考虑负荷辨识商业应用这一块，老实说本文不适合真正的商业应用，不建议在这些文献中浪费时间。在实际的商业应用中，还是通过电流谐波等特征，用传统的信号处理方法比如模版匹配滤波、盲源分离、构造特征向量或矩阵等比较符合商业实际和经济成本，应考虑这些传统思路。）

第一次更新时间：2023年1月10日 20:50:39

第一次更新的内容：全文的分享，排版还没有空改

第二次更新时间：2023年2月24日 15:52:30

第二次更新的内容：排版还没有空改，在忙学位论文，增加一下本文适用读者的说明。

必读的综述

《Review on Deep Neural Networks Applied to Low-Frequency NILM》

如果你打算通过深度学习来研究NILM，这是一篇必读的综述。这篇综述的发表时间在2020年前后，包括了网上几乎全部的NILM公开数据集、论文及代码地址。我这篇整理，也是在这篇综述的基础上，增加一些额外收集到的NILM公开代码和论文。

论文名称及对应的代码地址

对于必读和比较重要的，我会特意在下面给出文字提示，其它的也会按需要加上注解。

主要基于CNN的（包括GAN、VAE、LSTM之类的）：

《Neural NILM: Deep Neural Networks Applied to Energy Disaggregation》

code:GitHub - OdysseasKr/neural-disaggregator: Code for NILM experiments using Neural Networks. Uses Keras/Tensorflow and the NILMTK.

GitHub - JackKelly/neuralnilm: Deep Neural Networks Applied to Energy Disaggregation

GitHub - maechler/nnilm: A reimplementation of Jack Kelly's rectangles neural network architecture based on Keras and the NILMToolkit.

推荐理由：深度学习用于NILM的开山之作，必读！

《Sequence-to-point learning with neural networks for nonintrusive load monitoring》

GitHub - MingjunZhong/NeuralNetNilm: Sequence-to-point learning for non-intrusive load monitoring (energy disaggregation)

GitHub - MingjunZhong/seq2point-nilm: Sequence-to-point learning for non-intrusive load monitoring

改进版本(进行剪枝)

Code: GitHub - JackBarber98/pruned-nilm: This repo provides four weight pruning algorithms for use in sequence-to-point energy disaggregation as well as three alternative neural network architectures.

paper: Lightweight Non-Intrusive Load Monitoring Employing Pruned Sequence-to-Point Learning | Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring

推荐理由：这里包括了seq2seq和seq2point两种方法，是很多论文的benchmark比较对象，必读！下面的改进版本可以暂时略过。

改进版本1：Structured Probabilistic Pruning

Wang, H., Zhang, Q., Wang, Y., Hu, H. (2018) Structured Probabilistic Pruning for Convolutional Neural Network Acceleration. Zhejiang University, China.

pdf: https://arxiv.org/pdf/1709.06994.pdf

改进版本2：Entropy-Based Pruning

Hur, C., Kang, S. (2018) Entropy-Based Pruning Method For Convolutional Neural Networks. The Journal of Supercomputing, 75:2950–2963.

pdf: https://link-springer-com/content/pdf/10.1007/s11227-018-2684-z.pdf

改进版本3：Relative Threshold Pruning

Asouri, A. H., Abdelrahman, T. S., Remedios, A. D. (2019) Retraining-Free Methods for Fast On-the-Fly Pruning of Convolutional Neural Networks. Neurocomputing, 370 56-59.

pdf: https://www.sciencedirect.com/science/article/abs/pii/S0925231219312019

《Sliding Window Approach for Online Energy Disaggregation Using Artificial Neural Networks》

code :https://github.com/OdysseasKr/online-nilm

pdf :https://dl.acm.org/doi/pdf/10.1145/3200947.3201011

NeuralNILM_Pytorch（这个不是论文）

注解：这个不是论文，应该是一个学生对其它论文的一个复现，基于pytorch框架，因为之前的工作很多时候都是用Tensorflow做的。但是这个仓库意外地包含了两篇中文核心的复现。

code: GitHub - Ming-er/NeuralNILM_Pytorch

下面是来自他github的截图

[5]基于 seq2seq 和 Attention 机制的居民用户非侵入式负荷分解

[8]基于卷积块注意力模型的非侵入式负荷分解算法

《Deep Latent Generative Models For Energy Disaggregation》

code: Bitbucket

注解：GAN

《WaveNILM: A causal neural network for power disaggregation from the complex power signal》

GitHub - picagrad/WaveNILM: WaveNILM as published at ICASSP 2019

注解：通过wavenet，把电力信号当成语音信号来处理？膨胀卷积，扩大感受野。

《A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks》

code: a3labShares / A3NeuralNILM · GitLab

《A Tree-Structured Neural Network Model for Household Energy Breakdown》

pdf: A Tree-Structured Neural Network Model for Household Energy Breakdown | The World Wide Web Conference

code: GitHub - yilingjia/TreeCNN-for-Energy-Breakdown: WWW19' A Tree-Structured Neural Network Model for Household Energy Breakdown

《Deep Learning-Based Energy Disaggregation and On/Off Detection of Household Appliances》

pdf : Deep Learning-Based Energy Disaggregation and On/Off Detection of Household Appliances | ACM Transactions on Knowledge Discovery from Data

code : GitHub - jiejiang-jojo/fast-seq2point

《EdgeNILM: Towards NILM on Edge devices》

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

code : https://github.com/EdgeNILM/EdgeNILM

《Lightweight Non-Intrusive Load Monitoring Employing Pruned Sequence-to-Point Learning》

paper: Lightweight Non-Intrusive Load Monitoring Employing Pruned Sequence-to-Point Learning | Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring

注解：剪枝；

《UNet-NILM: A Deep Neural Network for Multi-tasks Appliances State Detection and Power Estimation in NILM》

pdf: UNet-NILM | Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring

code : https://github.com/sambaiga/UNETNiLM

注解：Unet；多任务Multi-task；

《Exploring Time Series Imaging for Load Disaggregation》

pdf: https://mobile.aau.at/publications/bousbiat-buildsys20-imaging.pdf

code: https://github.com/BHafsa/image-nilm

注解：把NILM当成图像分类来做，挺有意思的，有好几篇也是这个思路，下面展开一下说明，代码在后面补充。

将一维序列数据转化为二维图像数据，把负荷识别当成进行图片分类来做，同时还有分灰度编码和彩色编码的图。

常见一维数据转二维的方法：

格拉米角场GAFs

example1: 《非侵入式负荷识别边缘计算颜色编码研究》（2020）

example2 : 《Imaging Time-Series for NILM》(2019)

2.马尔科夫变迁场MTF

example1:《Exploring Time Series Imaging for Load Disaggregation》(2020)

3.递归图Recurrence Plot

example1:Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks(2020)

4.短时傅里叶变换STFT

(这个应该有的，但是没记录)

5.V-I轨迹

example1:《**A feasibility study of automated plug-load identification from high-frequency measurements》(2015) **二值化V-I轨迹

example2**:《****Appliance classification using VI trajectories and convolutional neural networks》(2017) **灰度的V-I轨迹

example3**:《**Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning》(2019) 彩色的V-I轨迹

《Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification》

code: GitHub - lmssdd/TPNILM: Notebook for Temporal Pooling NILM

pdf: Applied Sciences | Free Full-Text | Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classificationz

注解：用到了语义分割里面比较出名的PSPNet来做多标签分类，可以读一下，代码也容易懂。

《Sequence To Subsequence Learning With Conditional Gan For Power Disaggregation》

pdf: Sequence-To-Subsequence Learning With Conditional Gan For Power Disaggregation | IEEE Conference Publication | IEEE Xplore

code: GitHub - DLZRMR/seq2subseq: Seq2subseq method for NILM

注解：GAN，生成对抗网络；

《Imaging Time-Series for NILM》

code: GitHub - LampriniKyrk/Imaging-NILM-time-series

《Non-Intrusive Load Monitoring with Fully Convolutional Networks》

pdf: https://arxiv.org/abs/1812.03915

code: GitHub - cbrewitt/nilm_fcn: Fully convolutional neural networks for non-intrusive load monitoring

《Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network》

pdf: https://www.mdpi.com/1996-1073/14/4/847/pdf

code: GitHub - antoniosudoso/attention-nilm: An Attention-based Deep Neural Network for Non-Intrusive Load Monitoring

《Sequence to point learning based on bidirectional dilated residual network for non-intrusive load monitoring》

pdf: https://www.sciencedirect.com/science/article/pii/S0142061521000776

code: https://github.com/linfengYang/BitcnNILM

注解：wavenet+空洞卷积，瞎搞的堆叠罢了。

《Generative Adversarial Networks and TransferLearning for Non-Intrusive Load Monitoring in Smart Grids》

pdf: Generative Adversarial Networks and Transfer Learning for Non-Intrusive Load Monitoring in Smart Grids | IEEE Conference Publication | IEEE Xplore

code :GitHub - Awadelrahman/GAN-NILM: GAN-NILM: Using Generative Adversarial Networks to perform Non-Intrusive Load Monitoring (aka load disaggregation)

注解：GAN+Transfer Learning（迁移学习），后面迁移学习相关的我再补充一篇

《Improved Appliance Classification in Non-Intrusive Load Monitoring using Weighted Recurrence Plots and Convolutional Neural Networks》

pdf: Energies | Free Full-Text | Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks

code: GitHub - sambaiga/WRG-NILM: Weighted Recurrence Graph for appliance classification

《Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring》

pdf: https://arxiv.org/pdf/2106.02352.pdf

code: GitHub - arx7ti/cold-nilm: The code to reproduce all the numerical results and the plots of the paper.

《Energy Disaggregation using Variational Autoencoders》

pdf: https://arxiv.org/pdf/2103.12177.pdf

code :GitHub - ETSSmartRes/VAE-NILM: Non-Intrusive Load Monitoring based on VAE model

注解：建议跑一下这个代码，挺仔细的，这几个作者做的实验，虽然其实就是一个VAE（变分自编码器），创新性一般的样子。

《Nonintrusive Residential Electricity Load Decomposition Based on Transfer Learning》

pdf: Sustainability | Free Full-Text | Nonintrusive Residential Electricity Load Decomposition Based on Transfer Learning

code : https://github.com/eyangs/transferNILM

注解：又是迁移学习。

《Adaptive Weighted Recurrence Graph for Appliance Recognition in Non-Intrusive Load Monitoring》

code : GitHub - sambaiga/AWRGNILM: Adaptive Recurrence Graph for Appliance classification in NILM.

pdf：https://ieeexplore.ieee.org/abstract/document/9144492

《Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network》

PDF：https://www.mdpi.com/1996-1073/13/16/4154/htm

code: https://github.com/sambaiga/MLCFCD

《DeepDFML-NILM: A New CNN-Based Architecture for Detection, Feature Extraction and Multi-Label Classification in NILM Signals》

code: GitHub - LucasNolasco/DeepDFML-NILM: A new CNN architecture to perform detection, feature extraction, and multi-label classification of loads, in non-intrusive load monitoring (NILM) approaches, with a single model for high-frequency signals.

pdf: DeepDFML-NILM: A New CNN-Based Architecture for Detection, Feature Extraction and Multi-Label Classification in NILM Signals | IEEE Journals & Magazine | IEEE Xplore

基于Transformer（BERT之类的）：

《A Bidirectional Transformer Model for Non-Intrusive Load Monitoring》

文献下载: http://nilmworkshop.org/2020/proceedings/nilm20-final88.pdf

Code: https://github.com/Yueeeeeeee/BERT4NILM

《Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid》

pdf: Sci-Hub | Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid | 10.3390/en14154649

code :https://github.com/vahit19/smart_grid

《Neural Fourier Energy Disaggregation》

pdf : https://www.mdpi.com/1424-8220/22/2/473

code : https://github.com/ChristoferNal/Neural-Fourier-Energy-Disaggregation

迁移学习Transfer Learning（包括前面提过的两篇）：

《Transfer Learning for Non-Intrusive Load Monitoring》

https://github.com/MingjunZhong/transferNILM

基于TensorFlow2.0版本的实现

GitHub - MingjunZhong/seq2point-nilm: Sequence-to-point learning for non-intrusive load monitoring

图卷积神经网络GNN

GitHub - LeoVogiatzis/GNN_based_NILM: Non Intrusive Load Monitoring based on Graph Neural Networks and Representation Learning

注解：22年6月的时候发现的，截止我发文的时候（2023年1月），也不知道这个作者发paper了没

隐马尔科夫HMM：

《Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring》

GitHub - smakonin/SparseNILM: The super-state hidden Markov model disaggregator that uses a sparse Viterbi algorithm for decoding. This project contains the source code that was use for my IEEE Transactions on Smart Grid journal paper.

《An Extensible Approach for Non-Intrusive Load Disaggregation With Smart Meter Data》

An Extensible Approach for Non-Intrusive Load Disaggregation With Smart Meter Data | IEEE Journals & Magazine | IEEE Xplore

GitHub - WilsonKong/siqpnilm

贝叶斯方法：

《Latent Bayesian melding for integrating individual and population models》

GitHub - MingjunZhong/LatentBayesianMelding: Latent Bayesian melding for non-intrusive load monitoring (energy disaggregation)

Graph Signal Processing

《On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing》

Code: GitHub - loneharoon/GSP_energy_disaggregator: This contains the energy disaggregation code based on Graph Signal Processing approach

pdf: https://ieeexplore.ieee.org/document/7457610

时间序列、Vector之类的：

《On time series representations for multi-label NILM》

pdf: On time series representations for multi-label NILM

code: GitHub - ChristoferNal/multi-nilm: Multi-NILM: Multi Label Non Intrusive Load Monitoring

《A Machine Learning Approach for NILM based on Odd Harmonic Current Vectors》

pdf :A Machine Learning Approach for NILM based on Odd Harmonic Current Vectors | IEEE Conference Publication | IEEE Xplore

code :GitHub - kbodurri/NILM: Code for our MPS 2019 paper entitled "A Machine Learning Approach for NILM based on Odd Harmonic Current Vectors"

杂七杂八说不清楚的其它代码（除了下面两篇论文，其它随便看看）

《Mixed-Integer Nonlinear Programming for State-based Non-Intrusive Load Monitoring》

code: GitHub - antoniosudoso/nilm-bqp: Mixed-Integer Nonlinear Programming for NILM

pdf: https://arxiv.org/abs/2106.09158

《Universal Non-Intrusive Load Monitoring (UNILM) Using Filter Pipelines, Probabilistic Knapsack, and Labelled Partition Maps》

pdf : https://arxiv.org/abs/1907.06299

code :GitHub - compsust/KP-NILM: Supervised NILM using multiple-choice knapsack problem (MCKP).

别人做的集合（论文、代码、数据集）

https://github.com/ch-shin/awesome-nilm

CS446 Project: Electric Load Identification using Machine Learning

code: GitHub - andydesh/nilm: Non intrusive load monitoring using machine learning

ZhangRaymond/Neural-NILM

vyokky/AAAI-NILM

《NILM: classification VS regression》

非侵入式负载监测（NILM）旨在预测家庭中家用电器的状态或消耗，只需知道汇总的电力负荷。NILM可以被表述为回归问题或最常见的分类问题。由智能电表收集的大多数数据集允许自然地定义回归问题，但相应的分类问题是一个派生问题，因为它需要通过阈值处理方法从电力信号转换为每个设备的状态。我们处理了三种不同的阈值处理方法来执行这一任务，讨论了它们在UK-DALE数据集的各种设备上的差异。我们分析了深度学习最先进的架构在回归和分类问题上的表现，介绍了选择最方便的阈值处理方法的标准。

code：https://github.com/UCA-Datalab/nilm-thresholding

Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

code: https://github.com/louisyuzhe/MachineLearning_NILM

注解：好像是国外某个大学生的本科毕业设计

标签：人工智能深度学习

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