Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring最新文献_第2页

An Open Problem: Energy Data Super-Resolution 一个开放的问题:能源数据超分辨率

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-18 DOI: 10.1145/3427771.3429995

Rithwik Kukunuri, Nipun Batra, Hongning Wang

引用次数: 2

Evaluation of low-complexity supervised and unsupervised NILM methods and pre-processing for detection of multistate white goods 评估用于检测多状态白色家电的低复杂度监督和无监督NILM方法和预处理

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-18 DOI: 10.1145/3427771.3427850

M. Khazaei, L. Stanković, V. Stanković

{"title":"Evaluation of low-complexity supervised and unsupervised NILM methods and pre-processing for detection of multistate white goods","authors":"M. Khazaei, L. Stanković, V. Stanković","doi":"10.1145/3427771.3427850","DOIUrl":"https://doi.org/10.1145/3427771.3427850","url":null,"abstract":"According to recent studies by the BBC and the Scottish Fire and Rescue Service, malfunctioning appliances, especially white goods, were responsible for almost 12,000 fires in Great Britain in just over 3 years, and almost everyday in 2019. The top three \"offenders\" are washing machines, tumble dryers and dishwashers, hence we will focus on these, generally challenging to disaggregate, appliances in this paper. The first step towards remotely assessing safety in the house, e.g., due to appliances not being switched off or appliance malfunction, is by detecting appliance state and consumption from the NILM result generated from smart meter data. While supervised NILM methods are expected to perform best on the house they were trained on, this is not necessarily the case with transfer learning on unseen houses; unsupervised NILM may be a better option. However, unsupervised methods in general tend to be affected by the noise in the form of unknown appliances, varying power levels and signatures. We evaluate the robustness of three well-performing (based on prior studies) low-complexity NILM algorithms in order to determine appliance state and consumption: Decision Tree and KNN (supervised) and DBSCAN (unsupervised), as well as different algorithms for preprocessing to mitigate the effect of noisy data. These are tested on two datasets with different levels of noise, namely REFIT and REDD datasets, resampled to 1 min resolution.","PeriodicalId":232655,"journal":{"name":"Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127131742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

On the Relationship between Seasons of the Year and Disaggregation Performance 论一年中的季节与分解性能的关系

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-18 DOI: 10.1145/3427771.3427856

João Góis, Christoph Klemenjak, L. Pereira

引用次数: 1

Explainable NILM Networks 可解释的NILM网络

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-18 DOI: 10.1145/3427771.3427855

David Murray, L. Stanković, V. Stanković

{"title":"Explainable NILM Networks","authors":"David Murray, L. Stanković, V. Stanković","doi":"10.1145/3427771.3427855","DOIUrl":"https://doi.org/10.1145/3427771.3427855","url":null,"abstract":"There has been an explosion in the literature recently on Nonintrusive load monitoring (NILM) approaches based on neural networks and other advanced machine learning methods. However, though these methods provide competitive accuracy, the inner workings of these models is less clear. Understanding the outputs of the networks help in improving the designs, highlights the relevant features and aspects of the data used for making the decision, provides a better picture of the accuracy of the models (since a single accuracy number is often insufficient), and also inherently provides a level of trust in the value of the provided consumption feedback to the NILM end-user. Explainable Artificial Intelligence (XAI) aims to address this issue by explaining these \"black-boxes\". XAI methods, developed for image and text-based methods, can in many cases interpret well the outputs of complex models, making them transparent. However, explaining time-series data inference remains a challenge. In this paper, we show how some XAI-based approaches can be used to explain NILM deep learning-based autoencoders inner workings, and examine why the network performs or does not perform well in certain cases.","PeriodicalId":232655,"journal":{"name":"Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129639451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

NILM based Energy Disaggregation Algorithm for Dairy Farms 基于NILM的奶牛场能量分解算法

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-12 DOI: 10.1145/3427771.3427846

Akhilesh Yadav, Anuj Sinha, A. Saidi, C. Trinkl, W. Zörner

引用次数: 8

BERT4NILM

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-12 DOI: 10.1145/3427771.3429390

Zhenrui Yue, Camilo Requena Witzig, Daniel Jorde, Hans A. Jacobsen

引用次数: 3

Stop 停止

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-12 DOI: 10.1145/3427771.3429388

Richard Jones, Christoph Klemenjak, S. Makonin, I. Bajić

{"title":"Stop","authors":"Richard Jones, Christoph Klemenjak, S. Makonin, I. Bajić","doi":"10.1145/3427771.3429388","DOIUrl":"https://doi.org/10.1145/3427771.3429388","url":null,"abstract":"In Non-Intrusive Load Monitoring (NILM), as in many other machine learning problems, significant computational resources and time are spent training models using as much data as possible. This is perhaps driven by the preconception that more data leads to more accurate models and, eventually, better performing algorithms. When has enough prior training been done? When has a NILM algorithm encountered new, unseen data? This work applies the notion of Bayesian surprise to answer these important questions for both, supervised and unsupervised algorithms. We compare the performance of several NILM algorithms to establish a suggested threshold on two combined measures of surprise: postdictive surprise and transitional surprise. We validate the use of transitional surprise by exploring the performance of a particular Hidden Markov Model as a function of surprise threshold. Finally, we explore the use of a surprise threshold as a regularization technique to avoid overfitting in cross-house performance. We provide preliminary insights and clear evidence showing a point of diminishing returns for model performance with respect to dataset size, which can have implications for future model development, dataset acquisition, as well as aiding in model flexibility during deployment.","PeriodicalId":232655,"journal":{"name":"Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114856011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Annoticity Annoticity

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-12 DOI: 10.1145/3427771.3427844

Benjamin Völker, Marc Pfeifer, Philipp Scholl, Bernd Becker

引用次数: 4

Edge computed NILM: a phone-based implementation using MobileNet compressed by Tensorflow Lite 边缘计算NILM:一个基于手机的实现，使用Tensorflow Lite压缩的MobileNet

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-12 DOI: 10.1145/3427771.3427852

Shamim Ahmed, Marc Bons

{"title":"Edge computed NILM: a phone-based implementation using MobileNet compressed by Tensorflow Lite","authors":"Shamim Ahmed, Marc Bons","doi":"10.1145/3427771.3427852","DOIUrl":"https://doi.org/10.1145/3427771.3427852","url":null,"abstract":"In the context of residential Non-intrusive load monitoring (NILM), the usual service deployment process consists of collecting data from a metering device to the cloud, run algorithms on the cloud and then display results in a Web front or in an App. This approach comes with two major problems: on the one hand, important resources are allocated to the cloud process (including maintenance) while selling the solution on a substantial subscription basis is still a challenge. On the other hand, end-users are more and more reluctant to see their personal data being uploaded. In order to propose an alternative, this research has focused on edge computed NILM, namely the possibility to run NILM algorithms on existing devices on the end-user side, such as a smart phone. A two-stage model development has been carried out to obtain good disaggregation accuracy with lower model size. In the first stage, an efficient deep learning algorithm (MobileNet) has been adopted to obtain an accurate and light weight model. In the second stage, TensorFlow Lite has been used to compress further, in order to reduce edge device memory usage and computing time. To deal with real-life diversity, we have built large and diverse training and testing sets based on a combination of HES, UKDALE and REFIT datasets. Disaggregation performance has been assessed for both models: before and after TensorFlow Lite compression. Comparative analysis has been performed to facilitate implementation choices.","PeriodicalId":232655,"journal":{"name":"Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124837122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Bayesian model of electrical heating disaggregation 电热分解的贝叶斯模型

Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring Pub Date : 2020-11-11 DOI: 10.1145/3427771.3427848

Franccois Culiere, Laetitia Leduc, A. Belikov

引用次数: 7