#6036. Parameter estimation of unknown properties using transfer learning from virtual to existing buildings

June 2027publication date
Proposal available till 28-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Architecture;
Modeling and Simulation;
Building and Construction;
Computer Science Applications;
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Abstract:
This study proposes a transfer learning (TL)-based inverse modelling to identify unknown building properties. This study examines the transfer from virtual buildings to existing buildings, especially for identifying wall U-value, HVAC efficiency and lighting power density (LPD). For this purpose, synthetic data were generated from simulation results of sampled EnergyPlus models, and then we developed artificial neural network (ANN) models using this data. By adopting TL, the ANN models were transferred to the domain of existing buildings and evaluated on 61 existing buildings.
Keywords:
artificial neural network; building energy; calibration; machine learning; parameter estimation; Transfer learning

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