Uncertainty Quantification In Predictive Modelling Of Heat Demand Using Reduced-order Grey Box Models

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Title: Uncertainty Quantification In Predictive Modelling Of Heat Demand Using Reduced-order Grey Box Models
Authors: Shamsi, Mohammad HarisAli, UsmanAlshehri, FawazO'Donnell, James
Permanent link: http://hdl.handle.net/10197/12262
Date: 4-Sep-2019
Online since: 2021-06-21T11:21:14Z
Abstract: 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.
Funding Details: University College Dublin
Type of material: Conference Publication
Publisher: IBPSA
Copyright (published version): 2019 the Authors
Keywords: Building energy modellingHeating demandsUncertainty analysis
DOI: 10.26868/25222708.2019.210246
Other versions: http://www.ibpsa.org/building-simulation-2019
Language: en
Status of Item: Peer reviewed
Is part of: Corrado, V., Fabrizio, E., Gasparella, A., and Patuzzi, F. (eds.). Building Simulation 2019
Conference Details: The 16th International Building Simulation Association (Building Simulation 2019), Rome, Italy, 2-4 September 2019
ISBN: 9781775052012
ISSN: 2522-2708
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Mechanical & Materials Engineering Research Collection
Energy Institute Research Collection

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