A Study on the Trade-off between Energy Forecasting Accuracy and Computational Complexity in Lumped Parameter Building Energy Models

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Title: A Study on the Trade-off between Energy Forecasting Accuracy and Computational Complexity in Lumped Parameter Building Energy Models
Authors: Andrade-Cabrera, Carlos
De Rosa, Mattia
Kathirgamanathan, Anjukan
Kapetanakis, Dimitrios-Stavros
Finn, Donal
Permanent link: http://hdl.handle.net/10197/9663
Date: 9-May-2018
Online since: 2019-03-22T12:51:54Z
Abstract: The development of urban scale cost-optimal retrofit decision making requires the development of simplified building energy models which provide satisfactory energy prediction accuracy while remaining tractable when implemented at scale. Lumped parameter building energy models are computationally efficient representations of building thermal performance. The current paper introduces a user-led iterative model reduction methodology which identifies potential trade-offs between model complexity (thus computational requirements) and energy estimation accuracy. Model complexity is progressively reduced using an energy performance criterion prior to model trimming. The methodology is applied to a building energy model of a mixed-use building, which is developed in the EnergyPlus Building Energy Model Simulation (BEMS) environment. The energy performance of the building is evaluated using a linear energy minimisation problem. The proposed methodology shows a potential reduction by half of the model complexity is possible, while retaining annual energy estimation errors below 10% for the target building.
Funding Details: European Commission Horizon 2020
Science Foundation Ireland
Type of material: Conference Publication
Publisher: IBPSA
Keywords: Reduced order modellingModel order reductionModel calibrationEnergy estimation
Other versions: http://esim2018.etsmtl.ca/
Language: en
Status of Item: Peer reviewed
Conference Details: The 10th Canada conference of International Building Performance Simulation Association (eSim 2018), Montreal, Canada, 9-10 May 2018
Appears in Collections:Mechanical & Materials Engineering Research Collection
ERC Research Collection
Energy Institute Research Collection

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