Short-term Load Forecasting with Dense Average Network
This work addresses power load forecasting for the power industry, offering incremental improvements in accuracy that can lead to significant cost savings.
The paper tackles short-term power load forecasting by proposing a dense average network, which improves prediction accuracy on two public datasets and demonstrates robustness to input disturbances.
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.