Data-driven models based on thermal resistor-capacitor networks (RC models) are useful in enhancing the energy performance of buildings. This paper presents a simple yet effective methodology to obtain reliable RC models for thermal dynamics of houses. In this methodology, complex preliminary model structures are first created based on physical principles and then simplified by progressively removing non-identifiable parameters to obtain the most suitable model structures. Two important techniques are adopted in the simplification process: (1) a genetic algorithm is employed during model training to ensure satisfactory fitting ability of large model structures; (2) asymptotic confidence intervals are calculated for parameter estimates and used to define parameter non-identifiability. The methodology is illustrated using a case study of a low energy house. The case study shows that the uncertainty of the obtained model is low, and the model parameters can be easily physically interpreted. Furthermore, validations of the simplified model structure show that there is no evident decrease in the fitting accuracy and the obtained model is able to characterize temperature differences between adjacent thermal zones. (C) 2019 Elsevier B.V. All rights reserved.