A short-term energy consumption forecasting method for attention mechanisms based on spatio-temporal deep learning

Abstract

Short-term energy consumption forecasting is the foundation of anomaly detection, scheduling and energy-saving control in manufacturing system. Existing methods do not reasonably distinguish between energy consumption nodes, but rather adopt overall prediction, resulting in poor prediction of target nodes. Consequently, this paper proposes a short-term energy consumption prediction method for the Attention Mechanism based on spatio-temporal deep learning (ST-Attention). Convolutional Neural Network (CNN) is utilized to extract feature extraction to acquire local high-dimensional features; Local long-term trend features are extracted using Gate Recurrent Unit (GRU) to reduce the inaccuracy of prediction due to the mixing of raw data;Finally, the Attention Mechanism is utilized to dynamically assign the weight of energy consumption nodes attribute to effectively deals with the difference, anisotropy and correlation between energy consumption nodes. The attention mechanism fuses extracted temporal and spatial features through weight assignment to enable short-term energy consumption prediction for manufacturing systems. The actual data collected from the aluminum profile factory is used to verify that the ST-Attention model providing better performance for short-term energy consumption prediction of the manufacturing system.