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

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

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
• Acceptance Date: Mar 6, 2020
• Online Publication Date: Mar 19, 2020
• Publication Date: Sep 1, 2020
• Deposit Date: Apr 7, 2020
• Publicly Available Date: Mar 20, 2021
• Journal: Journal of Building Engineering
• Electronic ISSN: 2352-7102
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 31
• Article Number: 101339
• DOI: https://doi.org/10.1016/j.jobe.2020.101339
• Keywords: Pattern identification; Gaussian mixture model clustering; Cooling load; Data visualization; Energy management
• Public URL: https://uwe-repository.worktribe.com/output/5866093
• 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.