Existing occupancy prediction models for residential buildings often lack the reasonable consideration of resident diversity, which generally results in poor prediction accuracy and limited applicability. To address this issue, this study proposes a Resident-differentiated, Markov Chain Occupancy Prediction Model with Cluster (RMCPMC) analysis to fully consider the resident diversity so as to improve the model predictive performance. First, Spearman correlation analysis is employed to identify the correlation between different influencing factors (i.e. resident characteristics) and total occupancy duration. The identified correlation coefficients are used as the weights for corresponding factors, and cluster analysis is subsequently performed to classify residents into different groups. Finally, RMCPMC models are established for obtained clusters to predict the occupancy pattern. To validate the performance of the proposed model, it is applied to the UK Time Use Survey (TUS) dataset and its performance is compared with the conventional Markov Chain（MC） model. Compared with the conventional MC model, the Mean Absolute Error and the Root Mean Square Error of the prediction accuracy decrease by 20.57% and 15.35%, respectively. The results indicate a significant improvement in model prediction accuracy through reasonably considering resident diversity and their impacts on occupancy patterns.