Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Tuong Le | - |
dc.contributor.author | Minh Thanh Vo | - |
dc.contributor.author | Tung Kieu | - |
dc.contributor.author | Hwang, Eenjun | - |
dc.contributor.author | Rho, Seungmin | - |
dc.contributor.author | Baik, Sung Wook | - |
dc.date.accessioned | 2021-08-31T01:34:46Z | - |
dc.date.available | 2021-08-31T01:34:46Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2020-05 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/56160 | - |
dc.description.abstract | Electric energy consumption forecasting is an interesting, challenging, and important issue in energy management and equipment efficiency improvement. Existing approaches are predictive models that have the ability to predict for a specific profile, i.e., a time series of a whole building or an individual household in a smart building. In practice, there are many profiles in each smart building, which leads to time-consuming and expensive system resources. Therefore, this study develops a robust framework for the Multiple Electric Energy Consumption forecasting (MEC) of a smart building using Transfer Learning and Long Short-Term Memory (TLL), the so-called MEC-TLL framework. In this framework, we first employ a k-means clustering algorithm to cluster the daily load demand of many profiles in the training set. In this phase, we also perform Silhouette analysis to specify the optimal number of clusters for the experimental datasets. Next, this study develops the MEC training algorithm, which utilizes a cluster-based strategy for transfer learning the Long Short-Term Memory models to reduce the computational time. Finally, extensive experiments are conducted to compare the computational time and different performance metrics for multiple electric energy consumption forecasting on two smart buildings in South Korea. The experimental results indicate that our proposed approach is capable of economical overheads while achieving superior performances. Therefore, the proposed approach can be applied effectively for intelligent energy management in smart buildings. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.subject | PREDICTION | - |
dc.subject | FRAMEWORK | - |
dc.title | Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Hwang, Eenjun | - |
dc.identifier.doi | 10.3390/s20092668 | - |
dc.identifier.scopusid | 2-s2.0-85084253242 | - |
dc.identifier.wosid | 000537106200232 | - |
dc.identifier.bibliographicCitation | SENSORS, v.20, no.9 | - |
dc.relation.isPartOf | SENSORS | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 20 | - |
dc.citation.number | 9 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | FRAMEWORK | - |
dc.subject.keywordAuthor | multiple electric energy consumption forecasting | - |
dc.subject.keywordAuthor | long short-term memory networks | - |
dc.subject.keywordAuthor | transfer learning | - |
dc.subject.keywordAuthor | the cluster-based strategy for transfer learning | - |
dc.subject.keywordAuthor | intelligent energy management system | - |
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.