A framework for uncertainty quantification in building heat demand simulations using reduced-order grey-box energy models

The sophistication of building energy performance tools has significantly increased the number of user inputs and parameters used to define energy models. There are numerous sources of uncertainty in model parameters which exhibit varied characteristics. Therefore, uncertainty analysis is crucial to ensure the validity of simulation results when assessing and predicting the performance of complex energy systems, especially in the absence of adequate experimental or real-world data. Furthermore, different kinds of uncertainties are often propagated using similar methods, which leads to a false sense of validity. A comprehensive framework to systematically identify, quantify and propagate these uncertainties is missing. The main aim of this research is to formulate an uncertainty framework to identify and quantify different types of uncertainties associated with reduced-order grey box energy models used in heat demand predictions of the building stock. The study introduces an integrated uncertainty approach based on a copula-based theory and nested Fuzzy Monte Carlo approach to address the correlations and separate the different kinds of uncertainties. Nested Fuzzy Monte-Carlo approach coupled with Latin Hypercube Sampling is used to propagate these uncertainties. Results signify the importance of uncertainty identification and propagation within an energy system and thus, an integrated approach to uncertainty quantification is necessary to maintain the relevance of developed building simulation models. Moreover, segregation of relevant uncertainties aids the stakeholders in supporting risk-related design decisions for improved data collection or model improvement.