Abstract: Based on the trends, periodicity and calendar features of power load, it is realized to accurately predict the annual daily average load curves considering the dynamic time anchors and typical feature constraints, Firstly, a dynamic time anchor matrix is built based on the calendar association between the historical year and the target year. Then, based on the historical annual daily average load shape-factor curves obtained after normalization and periodic smoothing treatment, it is proposed to use the DTA-Soft-DBA to predict the target year's daily average load shape-factor curve. After inverse normalization and inverse periodic smoothing treatment, the annual daily average load prediction curves are obtained by correcting the typical feature constraints with the predicted value of power and electricity features. The case study results of an area in China show that the proposed method has higher prediction accuracy, and the results are consistent with the predicted values of typical features and the temporal variations within the target year. The proposed method can effectively integrate the common rules of historical sample time series with different calendar characteristics, which is reasonable and interpretable.