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Effective and scalable methods for modelling existing non-domestic buildings and their HVAC systems under uncertainty continue to be instrumental in risk-conscious building performance assessment, recommissioning, and retrofit practice. This study makes such an attempt for large buildings with radiator system with a modelling method that builds upon detailed state space models of radiator-heated spaces, an archetype-based spatial reduction approach to modelling an entire building, a steady-state model of heat distribution subsystem, and explicit quantification of uncertainties in the above models. The capability and efficacy of the method were demonstrated by a case study on a building section on campus. The results show that the proposed method can effectively capture the detailed dynamic building heat transfer phenomena in individual spaces and is scalable to large complex buildings with moderate model complexity and computation cost.

There is currently no established methodology for 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.

A building energy simulation relies on accurate parameterisation of occupant-related internal loads to simulate a realistic energy balance within a building. The internal loads are inextricably linked to occupant behaviour, both directly through the contribution of occupant heat output to thermal energy balance and indirectly via the interactions between occupants, appliances and building services. While occupancy itself is difficult to measure directly, most buildings possess a wealth of data in the form of monitored electricity consumption in varying degrees of resolution. These data, particularly plug loads, may be used to inform the model of occupant-related internal loads. Different approaches to parameterisation of plug loads have been investigated, with the purpose of exploring the conditions that might lead to preference of one approach over another. The models have been tested through a case study and simulation results have been compared against a range of response variables. Conclusions have been drawn as to the most important features of plug load parameterisation for a model to be used for forecasting future demand., Energy and Buildings, 123, ISSN:0378-7788, ISSN:1872-6178