Short-term Load Forecasting with Dense Average Network

Short-trem Load forecasting is of great significance to power system. In this paper, we propose a new connection, Dense Average connection, in which the outputs of all previous layers are averaged as the input of the next layer in a feedforward method.Compared with fully connected layer, the Dense Average connection does not introduce new training parameters.Based on the Dense Average connection,we build the Dense Average Network for load forecasting. In two public datasets and one real dataset, we verify the validity of the model.Compared with ANN, our proposed model has better convergence and prediction effect.Meanwhile, we use the ensemble method to further improve the prediction effect. In order to verify the reliability of the model, we also disturb the input of the model to different degrees. Experimental results show that the proposed model is very robust.