Uncertainty quantification in predictive modelling of heat demand using reduced-order grey box models

As building energy modelling becomes more sophisticated, the amount of user input and the number of parameters used to define the models continue to grow. There are numerous sources of uncertainty in these parameters especially when a modelling process is being performed before construction and commissioning. Therefore, uncertainty quantification is important in assessing and predicting the performance of complex energy systems, especially in absence of adequate experimental or real-world data. 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 prediction of the building stock. The uncertainties are characterized and then propagated using the Monte-Carlo sampling technique. Results signify the importance of uncertainty identification and propagation within a system and thus, an integrated approach to uncertainty quantification is necessary to maintain the relevance of developed models.