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The performance gap caused by occupant behaviour (OB) is one of the main challenges to the accuracy of building performance simulations (BPS) models. Calibration of BPS models has shown great improvements in tertiary and single residential buildings. Nevertheless, the calibration in collective residential buildings is still uncertain. This study aims to identify the opportunities and barriers to the calibration of collective residential building BPS models for the analysis of heating energy consumption. For this, the research calibrates a real case study of a social rental housing building located in northern Spain. The method involves the adjustment of input data based on OB clusters, developed by monitorization and survey data and the statistical comparison of the results of normative models, calibrated models and real data. The results show an average improvement of 67% in hourly indoor temperature and 16% in hourly heating energy consumption in calibrated models, but still with a considerable performance gap. The main barriers to a higher accuracy are the wide diversity and lack of uniformity of OB patterns, uncertainty of parameters, and use of auxiliary heating systems. However, deeper monitorization and survey campaigns with the use of OB clusters can be a promising opportunity.