Abstract: Urban power load forecasting is an important content of urban smart grid planning and scheduling.However,the pro-blem of data imbalance in urban power load forecasting poses a great challenge to urban power load forecasting.Traditional single-model-based methods can hardly solve the problem of data imbalance.The existing multi-model-based forecasting methods split the datasets into multiple sub-datasets according to the electricity load profiles,and then build multiple forecasting models for forecasting,which can solve the data imbalance problem to a certain extent,but there are problems such as high model construction cost and separation of the common electricity distribution characteristics among different distribution profiles.Based on this,this paper proposes a lighten urban electric load forecasting model(Lighten-DCSC-LSTM).It is constructed by introducing the idea of discrepancy compensation and short-term sampling contrastive loss on the basis of long and short-term memory networks,while building a shared feature extraction layer to reduce the model construction cost.Among them,the idea of discrepancy compensation compensates the prediction results of the main sequence prediction module by learning the differences between different power load distribution samples,and the short-term sampling contrastive loss regularizes the training of the model by the contrastive learning loss of the dynamic class center.To verify the performance of the proposed model,parameter tuning and comparison experiments are conducted.The results of the comparison experiments show that the model achieves good perfor-mance in the task of forecasting electricity loads.

Key words: Electricity load forecasting, Long-short term memory networks, Deep learning, Contrastive learning loss