Convolutional sequence to sequence non-intrusive load monitoring
Reads0
Chats0
TLDR
In this paper, a convolutional sequence to sequence nonintrusive load monitoring model is proposed to extract information from the sequences of aggregate electricity consumption and residual blocks are also introduced to refine the output of the neural network.Abstract:
A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this study. Gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are also introduced to refine the output of the neural network. The partially overlapped output sequences of the network are averaged to produce the final output of the model. The authors apply the proposed model to the reference energy disaggregation data set dataset and compare it with the convolutional sequence to point model in the literature. Results show that the proposed model is able to give satisfactory disaggregation performance for appliances with varied characteristics.read more
Citations
More filters
Journal ArticleDOI
Transfer Learning for Non-Intrusive Load Monitoring
TL;DR: Zhang et al. as discussed by the authors proposed two transfer learning schemes, appliance transfer learning (ATL) and cross-domain transfer learning(CTL), to recover source appliances from only the recorded mains in a household.
Posted Content
Transfer Learning for Non-Intrusive Load Monitoring
TL;DR: Two transfer learning schemes are proposed, i.e., appliance transfer learning (ATL) and cross-domain transferLearning (CTL), and the conclusion is that the seq2point learning is transferable.
Journal ArticleDOI
Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree
TL;DR: A novel non-intrusive appliance recognition system based on detecting events in the aggregated power signal using a novel and powerful scheme, applying multiscale wavelet packet tree to collect comprehensive energy consumption features, and adopting an ensemble bagging tree classifier is proposed.
Journal ArticleDOI
Scale- and Context-Aware Convolutional Non-Intrusive Load Monitoring
TL;DR: This paper boosts the accuracy of energy disaggregation with a novel neural network structure named scale- and context-aware network, which exploits multi-scale features and contextual information.
Journal ArticleDOI
Review on Deep Neural Networks Applied to Low-Frequency NILM
TL;DR: This paper reviews non-intrusive load monitoring approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e., data with sampling rates lower than the AC base frequency and does a performance comparison with respect to reported mean absolute error (MAE) and F1-scores.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Posted Content
Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: In this paper, the authors propose to use a soft-searching model to find the parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
Posted Content
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martín Abadi,Ashish Agarwal,Paul Barham,Eugene Brevdo,Zhifeng Chen,Craig Citro,Greg S. Corrado,Andy Davis,Jeffrey Dean,Matthieu Devin,Sanjay Ghemawat,Ian Goodfellow,Andrew Harp,Geoffrey Irving,Michael Isard,Yangqing Jia,Rafal Jozefowicz,Lukasz Kaiser,Manjunath Kudlur,Josh Levenberg,Dan Mané,Rajat Monga,Sherry Moore,Derek G. Murray,Chris Olah,Mike Schuster,Jonathon Shlens,Benoit Steiner,Ilya Sutskever,Kunal Talwar,Paul A. Tucker,Vincent Vanhoucke,Vijay K. Vasudevan,Fernanda B. Viégas,Oriol Vinyals,Pete Warden,Martin Wattenberg,Martin Wicke,Yuan Yu,Xiaoqiang Zheng +39 more
TL;DR: The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.