Identifying power load curves is essential to ensure the safety and energy efficiency of the power grid. However

existing algorithms for power load curve identification tasks often suffer from issues such as low recognition accuracy and robustness. To tackle these issues

a multi-model fusion ensemble learning method for power grid load curve recognition is proposed. Temporal convolutional network （TCN）

transformer

and light GBM models are adaptively improved to predict load curve categories

considering three dimensions：local

global and structural features. Then

predictions through stacking ensemble learning（EL）to refine overall accuracy are adaptively fused.Additionally

a truncated Gaussian distribution（TGD）data augmentation strategy is introduced

which models intra-class signal fluctuations to alleviate data category imbalances

thereby enhancing the robustness of the recognition model. Through simulation analysis

compared with methods such as XG Boost

LSTM

and MLP

this approach shows a significant improvement in power load classification accuracy.