Simplified data-driven models for model predictive control of residential buildings
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Hyeongseok | - |
dc.contributor.author | Heo, Yeonsook | - |
dc.date.accessioned | 2022-06-09T08:40:55Z | - |
dc.date.available | 2022-06-09T08:40:55Z | - |
dc.date.created | 2022-06-09 | - |
dc.date.issued | 2022-06-15 | - |
dc.identifier.issn | 0378-7788 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/141713 | - |
dc.description.abstract | Owing to recent advancements in Internet of Things technologies, data-driven model predictive control (MPC) has received significant research interest as a promising strategy to optimize building operation. As the MPC performance relies on the model prediction accuracy, complex building prediction models have been used in MPC applications, despite their high computational cost for optimization. This study examines whether linear-form prediction models are reliable to support the MPC of residential buildings equipped with single types of heating systems. This study developed linear-form models, namely an autoregressive with exogenous inputs (ARX) for predicting the indoor temperature and thresholdpiecewise models for the return and supply water temperatures. The MPC performance on the basis of the linear models was evaluated under varying prediction horizons and weights associated with objective attributes. A case study of a residential unit through the simulated virtual building showed that the proposed models achieved the high goodness-of fit values greater than 0.9. The resulting MPC framework achieved heating energy savings up to approximately 12% relative to a simple on/off thermostat or reduction of comfort violation magnitude less than 0.5 degrees C. The influences of weight and prediction horizon on MPC performance were also investigated.(c) 2022 Elsevier B.V. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE SA | - |
dc.subject | ENERGY OPTIMIZATION | - |
dc.subject | GENETIC ALGORITHM | - |
dc.subject | THERMAL COMFORT | - |
dc.subject | NEURAL-NETWORK | - |
dc.subject | PERFORMANCE | - |
dc.subject | INDOOR | - |
dc.subject | LOAD | - |
dc.subject | TEMPERATURE | - |
dc.subject | CHALLENGES | - |
dc.subject | BEHAVIOR | - |
dc.title | Simplified data-driven models for model predictive control of residential buildings | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Heo, Yeonsook | - |
dc.identifier.doi | 10.1016/j.enbuild.2022.112067 | - |
dc.identifier.scopusid | 2-s2.0-85129438178 | - |
dc.identifier.wosid | 000800422100007 | - |
dc.identifier.bibliographicCitation | ENERGY AND BUILDINGS, v.265 | - |
dc.relation.isPartOf | ENERGY AND BUILDINGS | - |
dc.citation.title | ENERGY AND BUILDINGS | - |
dc.citation.volume | 265 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Construction & Building Technology | - |
dc.relation.journalResearchArea | Energy & Fuels | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Construction & Building Technology | - |
dc.relation.journalWebOfScienceCategory | Energy & Fuels | - |
dc.relation.journalWebOfScienceCategory | Engineering, Civil | - |
dc.subject.keywordPlus | ENERGY OPTIMIZATION | - |
dc.subject.keywordPlus | GENETIC ALGORITHM | - |
dc.subject.keywordPlus | THERMAL COMFORT | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordPlus | INDOOR | - |
dc.subject.keywordPlus | LOAD | - |
dc.subject.keywordPlus | TEMPERATURE | - |
dc.subject.keywordPlus | CHALLENGES | - |
dc.subject.keywordPlus | BEHAVIOR | - |
dc.subject.keywordAuthor | Model predictive control | - |
dc.subject.keywordAuthor | Residential buildings | - |
dc.subject.keywordAuthor | Autoregressive with exogenous inputs | - |
dc.subject.keywordAuthor | model | - |
dc.subject.keywordAuthor | Threshold-piecewise model | - |
dc.subject.keywordAuthor | Prediction horizon | - |
dc.subject.keywordAuthor | Weight | - |
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.