ETRI-Knowledge Sharing Plaform

KOREAN
논문 검색
Type SCI
Year ~ Keyword

Detail

Journal Article Denoising Masked Autoencoder-Based Missing Imputation within Constrained Environments for Electric Load Data
Cited 6 time in scopus Download 130 time Share share facebook twitter linkedin kakaostory
Authors
Jaeik Jeong, Tai-Yeon Ku, Wan-Ki Park
Issue Date
2023-12
Citation
Energies, v.16, no.24, pp.1-18
ISSN
1996-1073
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.3390/en16247933
Abstract
With recent advancements in data technologies, particularly machine learning, research focusing on the enhancement of energy efficiency in residential, commercial, and industrial settings through the collection of load data, such as heat, electricity, and gas, has gained significant attention. Nevertheless, issues arising from hardware- or network-related problems can result in missing data, necessitating the development of management techniques to mitigate these challenges. Traditional methods for missing imputation face difficulties when operating in constrained environments characterized by short data collection periods and frequent consecutive missing. In this paper, we introduce the denoising masked autoencoder (DMAE) model as a solution to improve the handling of missing data, even in such restrictive settings. The proposed DMAE model capitalizes on the advantages of the denoising autoencoder (DAE), enabling effective learning of the missing imputation process, even with relatively small datasets, and the masked autoencoder (MAE), allowing for learning in environments with a high missing ratio. By integrating these strengths, the DMAE model achieves an enhanced performance in terms of missing imputation. The simulation results demonstrate that the proposed DMAE model outperforms the DAE or MAE significantly in a constrained environment where the duration of the training data is short, less than a year, and missing values occur frequently with durations ranging from 3 h to 12 h.
KSP Keywords
2 H, Data Collection, Electric load data, Energy efficiency, Enhanced performance, Management techniques, Missing data, Missing values, Traditional methods, denoising autoencoder, machine Learning
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
CC BY