This paper describes a novel method for common change detection in panel data emanating from smart electricity and water networks. The proposed method relies on a representation of the data by classes whose probabilities of occurrence evolve over time. This dynamics is assumed to be piecewise periodic due to the cyclic nature of the studied data, which allows the detection of change points. Our strategy is based on a hierarchical mixture of t-distributions which entails some robustness properties. The parameter estimation is performed using an incremental strategy, which has the advantage to allow the processing of large datasets. The experiments carried out on realistic data showed the full relevance of the proposed method.