Learning Disentangled Representation of Residential Power Demand Peak via Convolutional-Recurrent Triplet Network

Hyung Jun Moon, Seok Jun Bu, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the time-series models for predicting residential energy consumption, the energy properties collected through multiple sensors usually include irregular and seasonal factors. The irregular pattern resulting from them is called peak demand, which is a major cause of performance degradation. In order to enhance the performance, we propose a convolutional-recurrent triplet network to learn and detect the demand peaks. The proposed model generates the latent space for demand peaks from data, which is transferred into convolutional neural network-long short-term memory (CNN-LSTM) to finally predict the future power demand. Experiments with the dataset of UCI household power consumption composed of a total of 2,075,259 time-series data show that the proposed model reduces the error by 23.63% and outperforms the state-of-the-art deep learning models including the CNN-LSTM. Especially, the proposed model improves the prediction performance by modeling the distribution of demand peaks in Euclidean space.

Original languageEnglish
Title of host publicationProceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
EditorsGiuseppe Di Fatta, Victor Sheng, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
PublisherIEEE Computer Society
Pages757-761
Number of pages5
ISBN (Electronic)9781728190129
DOIs
Publication statusPublished - 2020 Nov
Event20th IEEE International Conference on Data Mining Workshops, ICDMW 2020 - Virtual, Sorrento, Italy
Duration: 2020 Nov 172020 Nov 20

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2020-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference20th IEEE International Conference on Data Mining Workshops, ICDMW 2020
Country/TerritoryItaly
CityVirtual, Sorrento
Period20/11/1720/11/20

Bibliographical note

Funding Information:
ACKNOWLEDGEMENTS This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University)) and Korea Electric Power Corporation (Grant number: R18XA05).

Publisher Copyright:
© 2020 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

Fingerprint

Dive into the research topics of 'Learning Disentangled Representation of Residential Power Demand Peak via Convolutional-Recurrent Triplet Network'. Together they form a unique fingerprint.

Cite this