Skip to main content

Research Repository

Advanced Search

3D pattern identification approach for cooling load profiles in different buildings

Luo, X. J.; Oyedele, Lukumon O.; Akinade, Olugbenga ; Ajayi, Anuoluwapo O.

Authors

Xiaojun Luo Xiaojun.Luo@uwe.ac.uk
Research Fellow - Evolutionary Computing and Optimisation

Olugbenga Akinade Olugbenga.Akinade@uwe.ac.uk
Associate Professor - AR/VR Development with Artificial Intelligence



Abstract

© 2020 Elsevier Ltd Building energy conservation has gained increasing concern owing to its large portion of energy consumption and great potential of energy saving. In-depth understanding of representative patterns of daily cooling load profile will facilitate effective building energy system scheduling, fault detection and diagnosis, as well as demand and supply side management. In this study, a novel three-stage approach is proposed for pattern identification of cooling load profiles in different types of buildings. The three stages include data preparation, data clustering and data visualization. The initial measurement in the building energy management system is conducted at the time step of 15 min. To further explore the characteristics of the building cooling load trend, 1-h mean pattern, 4-h mean pattern and daily statistical information (i.e. average, minimum and maximum values) of cooling load are also adopted for data clustering, respectively. To test the generality and robustness of the proposed approach, one-year historical measurement data collected from the practical chilled water system in two different buildings are adopted, respectively. The analysis demonstrates that the 3D pattern identification approach can effectively discover the representative characteristics of the daily cooling load profiles in both buildings. It is also expected that the proposed 3-stage pattern identification approach is general in adoption and can be potentially adopted in various types of buildings in different climate zones.

Journal Article Type Article
Publication Date Sep 1, 2020
Journal Journal of Building Engineering
Electronic ISSN 2352-7102
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 31
Pages 101339
APA6 Citation Luo, X. J., Oyedele, L. O., Akinade, O., & Ajayi, A. O. (2020). 3D pattern identification approach for cooling load profiles in different buildings. Journal of Building Engineering, 31, 101339. https://doi.org/10.1016/j.jobe.2020.101339
DOI https://doi.org/10.1016/j.jobe.2020.101339
Keywords Pattern identification; Gaussian mixture model clustering; Cooling load; Data visualization; Energy management
Additional Information This article is maintained by: Elsevier; Article Title: 3D pattern identification approach for cooling load profiles in different buildings; Journal Title: Journal of Building Engineering; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.jobe.2020.101339; Content Type: article; Copyright: © 2020 Elsevier Ltd. All rights reserved.
;