Framework for emulation and uncertainty quantification of a stochastic building performance simulator

P. Wate, M. Iglesias, V. Coors and D. Robinson

Applied Energy, 2020, vol. 258, issue C

Abstract: A good framework for the quantification and decomposition of uncertainties in dynamic building performance simulation should: (i) simulate the principle deterministic processes influencing heat flows and the stochastic perturbations to them, (ii) quantify and decompose the total uncertainty into its respective sources, and the interactions between them, and (iii) achieve this in a computationally efficient manner. In this paper we introduce a new framework which, for the first time, does just that. We present the detailed development of this framework for emulating the mean and the variance in the response of a stochastic building performance simulator (EnergyPlus co-simulated with a multi agent stochastic simulator called No-MASS), for heating and cooling load predictions. We demonstrate and evaluate the effectiveness of these emulators, applied to a monozone office building. With a range of 25–50 kWh/m2, the epistemic uncertainty due to envelope parameters dominates over aleatory uncertainty relating to occupants' interactions, which ranges from 6–8 kWh/m2, for heating loads. The converse is observed for cooling loads, which vary by just 3 kWh/m2 for envelope parameters, compared with 8–22 kWh/m2 for their aleatory counterparts. This is due to the larger stimuli provoking occupants' interactions. Sensitivity indices corroborate this result, with wall insulation thickness (0.97) and occupants' behaviours (0.83) having the highest impacts on heating and cooling load predictions respectively. This new emulator framework (including training and subsequent deployment) achieves a factor of c.30 reduction in the total computational budget, whilst overwhelmingly maintaining predictions within a 95% confidence interval, and successfully decomposing prediction uncertainties.

Keywords: Gaussian process emulator; Building performance; Stochasticity; Uncertainty quantification and decomposition (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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DOI: 10.1016/j.apenergy.2019.113759

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