Sliding window-based LightGBM model for electric load forecasting using anomaly repair
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Sungwoo | - |
dc.contributor.author | Jung, Seungmin | - |
dc.contributor.author | Jung, Seungwon | - |
dc.contributor.author | Rho, Seungmin | - |
dc.contributor.author | Hwang, Eenjun | - |
dc.date.accessioned | 2022-02-15T05:42:28Z | - |
dc.date.available | 2022-02-15T05:42:28Z | - |
dc.date.created | 2022-02-09 | - |
dc.date.issued | 2021-11 | - |
dc.identifier.issn | 0920-8542 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/135828 | - |
dc.description.abstract | Smart grids have attracted much attention recently for their potential to reduce power system operating and management costs. Smart grid core components include energy storage, renewable energy source(s), and smart meters. Smart meters collect diverse data regarding smart grid operation, which can lead to inefficient operation if the meter data are damaged or tampered with during collection or transmission. Therefore, it is important to identify abnormalities in smart grid data and process them accordingly. Various anomaly detection models have been proposed using statistical methods, but they cannot detect some anomaly patterns accurately, and the models generally did not consider repair strategies for the detected anomalies. Anomaly repair should be included with model training to improve forecasting performance. This paper proposes a robust sliding window-based LightGBM model for short-term load forecasting using anomaly detection and repair. We first show how to detect anomalies using a variational autoencoder and then how they can be repaired using a random forest method. Finally, we verify that the proposed sliding window-based LightGBM achieves superior forecasting performance in combination with anomaly detection and repair. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | TIME-SERIES | - |
dc.subject | SENSOR TECHNOLOGY | - |
dc.subject | BIG DATA | - |
dc.subject | INTERPOLATION | - |
dc.subject | FRAMEWORK | - |
dc.title | Sliding window-based LightGBM model for electric load forecasting using anomaly repair | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hwang, Eenjun | - |
dc.identifier.doi | 10.1007/s11227-021-03787-4 | - |
dc.identifier.scopusid | 2-s2.0-85104596619 | - |
dc.identifier.wosid | 000640153400003 | - |
dc.identifier.bibliographicCitation | JOURNAL OF SUPERCOMPUTING, v.77, no.11, pp.12857 - 12878 | - |
dc.relation.isPartOf | JOURNAL OF SUPERCOMPUTING | - |
dc.citation.title | JOURNAL OF SUPERCOMPUTING | - |
dc.citation.volume | 77 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 12857 | - |
dc.citation.endPage | 12878 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Hardware & Architecture | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | BIG DATA | - |
dc.subject.keywordPlus | FRAMEWORK | - |
dc.subject.keywordPlus | INTERPOLATION | - |
dc.subject.keywordPlus | SENSOR TECHNOLOGY | - |
dc.subject.keywordPlus | TIME-SERIES | - |
dc.subject.keywordAuthor | Anomaly detection | - |
dc.subject.keywordAuthor | Data repair | - |
dc.subject.keywordAuthor | Electric load forecasting | - |
dc.subject.keywordAuthor | LightGBM | - |
dc.subject.keywordAuthor | Random forest | - |
dc.subject.keywordAuthor | Sliding window | - |
dc.subject.keywordAuthor | Variational autoencoder | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.