Load Disaggregation Based on Sequence-to-point Network with Unsupervised Pre-training

Shuyi Chen*, Bochao Zhao, Wenpeng Luan, Mingjun Zhong

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

Abstract

It is known that successful load disaggregation via deep learning relies on a large number of labeled data to train the deep neural networks. However, it is hard and expensive to acquire a large amount of appliance-level power data or ON/OFF labels. For overcoming such weakness, in this paper, unsupervised pre-training is applied to the state-of-the-art sequence-to-point (s2p) deep learning approach for NILM, where labeling is not required in pre-training. In the proposed method, the s2p deep neural network is initially pre-trained on unlabeled aggregate power readings for other houses, and then fine-tuned on a small set of aggregate power for the target house labeled by individual appliance monitoring. Finally, the generated network is applied to testing with the aggregate for the target house as input and outputting the power signal for the target load. The proposed method is validated on the UK REFIT dataset1, benchmark with s2p in two popular evaluation metrics. Experimental results show that the proposed unsupervised pre-training effectively improves NILM performance of the deep neural network with a lack of labeled training data.

Original languageEnglish
Title of host publication5th IEEE Conference on Energy Internet and Energy System Integration
Subtitle of host publicationEnergy Internet for Carbon Neutrality, EI2 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3224-3229
Number of pages6
ISBN (Electronic)9781665434256
DOIs
Publication statusPublished - 2021
Event5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021 - Taiyuan, China
Duration: 22 Oct 202125 Oct 2021

Publication series

Name5th IEEE Conference on Energy Internet and Energy System Integration: Energy Internet for Carbon Neutrality, EI2 2021

Conference

Conference5th IEEE Conference on Energy Internet and Energy System Integration, EI2 2021
Country/TerritoryChina
CityTaiyuan
Period22/10/2125/10/21

Keywords

  • deep neural network
  • non-intrusive load monitoring
  • sequence-to-point learning
  • unsupervised pre-training

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