Parameter estimation of internal thermal mass of building dynamic models using genetic algorithm

Abstract

Building thermal transfer models are essential to predict transient cooling or heating requirements for performance monitoring, diagnosis and control strategy analysis. Detailed physical models are time consuming and often not cost effective. Black box models require a significant amount of training data and may not always reflect the physical behaviors. In this study, a building is described using a simplified thermal network model. For the building envelope, the model parameters can be determined using easily available physical details. For building internal mass having thermal capacitance, including components such as furniture, partitions etc., it is very difficult to obtain detailed physical properties. To overcome this problem, this paper proposes to present the building internal mass with a thermal network structure of lumped thermal mass and estimate the lumped parameters using operation data. A genetic algorithm estimator is developed to estimate the lumped internal thermal parameters of the building thermal network model using the operation data collected from site monitoring. The simplified dynamic model of building internal mass is validated in different weather conditions.