3D pattern identification approach for cooling load profiles in different buildings. (September 2020)

• Record Type: Journal Article
• Authors: Luo, X.J.; Oyedele, Lukumon O.; Olugbenga, Olugbenga O.; Ajayi, Anuoluwapo O.
• Is Part Of: Journal of building engineering. Volume 31(2020)
• Publication Date: 2020-09
• Subjects: Pattern identification -- Gaussian mixture model clustering -- Cooling load -- Data visualization -- Energy management; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2020.101339
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: 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. Graphical abstract: Image 1 Highlights: A novel three-stage approach is proposed for pattern identification of cooling load profiles. The three stages include data preparation, data clustering and data visualization. The different patterns including 15 min measurement, 1-h mean, 4-h mean and daily statistics of cooling load are adopted. The approach is robust and generalized in different types of buildings. One-year historical measurement data collected from two practical buildings were adopted for validation purpose.