Abstract
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.
Highlights
No building energy simulation is complete without a specification of internal loads including electricity consumption due to plug loads and lighting and the thermal contribution of the building occupants
This paper describes the development of a bottom-up datacentric model for plug loads for different types of space use in non-domestic buildings
A Functional Data Analysis approach has been used for analysis of metered electricity consumption due to plug loads for the William Gates building in Cambridge, UK, which is sub-metered at a high spatial resolution, with each zone hosting a different spaceuse type
Summary
No building energy simulation is complete without a specification of internal loads including electricity consumption due to plug loads and lighting and the thermal contribution of the building occupants. The study by Mahdavi, Tahmasebi, and Kayalar (2016) highlights the need for better models of plug loads for building performance simulation and explores detailed monitored occupancy and plug load data for a selected office. They demonstrate that while simple models may be adequate for predictions of aggregate demand, probabilistic methods are better at capturing the time dynamics
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