Iterative Inverse Model Coupled to Genetic Algorithm for Calibration of Thermal Buildings Simulation Models

Abstract

In this study, an iterative inverse model based on optimization with a genetic algorithm is implemented for the calibration and validation of computational simulation models of the thermal performance of buildings. This model dynamically adjusts air thermal resistances, thermal and solar absorbance of exterior materials, air infiltration, and convective coefficient to minimize discrepancies between measured and simulated air temperatures. This meticulous approach ensures accurate calibration and effective evaluation of the model’s thermal and energy performance, providing valuable information for the optimization of building energy design strategies. Considered as a case study are buildings built in Bulgaria, Sofia, within the framework of the NRG STORAGE project (Integrated porous cementicious Nanocomposites in non-Residential building envelopes for Green active/passive energy STORAGE).