Fast and Precise Energy Consumption Prediction Based on Fully Convolutional Attention Res2Net

Abstract

Energy consumption prediction has been poured lots of attention due to its importance in energy planning, management, conservation, etc. This paper proposes a convolutional network called Fully Convolutional Attention Res2Net (FCARN) based on Res2Net for fast and precise energy consumption prediction. Based on the thought of attention mechanism, gate whose value is calculated based on global feature maps are applied in the Res2Net. It assigns weights to the feature maps thus enabling the model to focus on the more important part. We conducted experiments and evaluated our model on appliances energy prediction dataset. As the thought of the gates is similar to Squeeze-and-Excitation Net (SENet), we compare our model with Res2Net, Res2Net combined with SENet and the existing state-of-the-art models. The results demonstrate the superiority and competitiveness of our model. In addition, we provide details of the training process.