Time series anomaly detection is crucial for energy consumption monitoring and management. In order to solve the problem that the periodic decomposition algorithm is easily affected by the inconsistency of patterns in time series, this paper proposes an anomaly detection method combining pattern clustering and periodic decomposition, which uses a density peak clustering algorithm to find the cluster centers and uses the cyclic distance measurement to solve the sub-sequence phase shift. To shield the influence between different patterns, the seasonal decomposition algorithm S-H-ESD (seasonal hybrid extreme studentized deviate) is used to find anomalies in clusters with the same periodic pattern, and the anomaly degrees of the detection results are evaluated and filtered. The experimental results show that the proposed method is effective and suitable for anomaly detection of complex periodic time series.