Paper Conference

Proceedings of eSim 2020: 11th Conference of IBPSA-Canada

     

Quantifying the scalability of reduced-order grey-box energy models for commercial building stock modeling

Mohammad Haris Shamsi 1,3, Usman Ali 1, Eleni Mangina 2, James O'Donnell 1
1 School of Mechanical & Materials Engineering, Energy Institute University College Dublin (UCD), Ireland
2 School of Computer Science and Informatics, University College Dublin (UCD), Ireland
3 University of Victoria, Canada


Abstract: Grey-box models are extensively employed in building energy simulations. However, the grey-box approach often leads to application and stakeholder specific models, for instance, the design approach of greybox modeling for commercial buildings differs on a case by case basis. Often, the network order limits the scalability of these networks. Reduced-order grey-box modeling approaches counter these limitations by achieving a trade off between model complexity and desired accuracy. This study, therefore, formulates a generalized methodology to quantify scalability associated with reduced-order grey-box models for heat demand modeling of commercial buildings. The devised methodology assesses model scalability through (1) scalability feature-definition, (2) model identification, (3) multi-level modeling and (4) KPI identification procedures. This study formulates a test-case of 10 buildings (on university campus) with varied operations to implement the devised methodology. Results indicate that model scalability directly associates with the nature of building operation. Furthermore, similar zone variables can effectively represent an entire building provided that the considered zone pre-dominantly occupies majority of the building’s indoor space.
Paper:
esim2020_1132