A data-centric bottom-up model for generation of stochastic internal load profiles based on space-use type

• Published in: Journal of building performance simulation Vol. 12; no. 5; pp. 620 - 636
• Main Authors: Ward, R. M., Choudhary, R., Heo, Y., Aston, J. A. D.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03-09-2019; Taylor & Francis Ltd
• Subjects: Computer simulation; Data analysis; Demand; Functional Data Analysis; plug loads; Principal Components; Spatial resolution; stochastic
• ISSN: 1940-1493; 1940-1507
• DOI: 10.1080/19401493.2019.1583287

There is currently no established methodology for the generation of synthetic stochastic internal load profiles for input into building energy simulation. In this paper, a Functional Data Analysis approach is used to propose a new data-centric bottom-up model of plug loads based on hourly data monitored at a high spatial resolution and by space-use type for a case-study building. The model comprises a set of fundamental Principal Components (PCs) that describe the structure of all data samples in terms of amplitude and phase. Scores (or weightings) for each daily demand profile express the contribution of each PC to the demand. Together the principal components and the scores constitute a structure-based model potentially applicable beyond the building considered. The results show good agreement between samples generated using the model and monitored data for key parameters of interest including the timing of the daily peak demand.