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.
|Title of host publication||Proceedings - 20th IEEE International Conference on Data Mining Workshops, ICDMW 2020|
|Editors||Giuseppe Di Fatta, Victor Sheng, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|Publication status||Published - 2020 Nov|
|Event||20th IEEE International Conference on Data Mining Workshops, ICDMW 2020 - Virtual, Sorrento, Italy|
Duration: 2020 Nov 17 → 2020 Nov 20
|Name||IEEE International Conference on Data Mining Workshops, ICDMW|
|Conference||20th IEEE International Conference on Data Mining Workshops, ICDMW 2020|
|Period||20/11/17 → 20/11/20|
Bibliographical noteFunding 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).
© 2020 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science Applications