  As an important part of the power system, power load forecasting directly
affects the national economy. The data shows that improving the load
forecasting accuracy by 0.01% can save millions of dollars for the power
industry. Therefore, improving the accuracy of power load forecasting has
always been the pursuing goals for a power system. Based on this goal, this
paper proposes a novel connection, the dense average connection, in which the
outputs of all preceding layers are averaged as the input of the next layer in
a feed-forward fashion. Based on dense average connection , we construct the
dense average network for power load forecasting. The predictions of the
proposed model for two public datasets are better than those of existing
methods. On this basis, we use the ensemble method to further improve the
accuracy of the model. To verify the reliability of the model predictions, the
robustness is analyzed and verified by adding input disturbances. The
experimental results show that the proposed model is effective and robust for
power load forecasting.
