With the popularity of smart meters

the aggregated load can be divided flexibly into different sizes according to different requirements and be predicted based on the measurement data. Due to the large difference in the scales of aggregated loads and the close relationship with the load characteristics of users

the traditional prediction method is no longer applicable. This paper proposes an aggregated load forecasting method based on gated recurrent unit(GRU)networks and model fusion. Firstly

load groups with similar load characteristics are clustered by the distributed spectral clustering algorithm

then grouping predictions are employed. Secondly

GRU is adopted as a meta-model to perform dynamic modeling of time series

and several different structures of GRU networks are fused by random forest algorithm to realize the load group forecast. Finally

the aggregated load forecast value can be obtained by summing prediction value of each group. Benefiting from the grouping prediction

dynamic time modeling

and model fusion technology

the proposed method can make full use of advantages of different model structures and discover the dynamic rule of time series. The proposed method achieves higher prediction accuracy and higher adaptability for aggregated load with different scales.