Randomized trial demonstrates improved accuracy in predicting regional building energy consumption using advanced AI methods, suggesting effective applications in energy management.
Key Points
The aim is to develop a method that effectively extracts spatiotemporal features of regional building energy consumption.
Utilized a residual network structure to separately extract temporal and spatial features.
Introduced a graph attention module and channel attention mechanism to optimize feature representation.
Conducted experiments to validate the method's effectiveness in capturing energy consumption patterns.
Achieved a prediction deviation as low as 2.41%.
Observed an average energy saving rate of 33.20%.
Demonstrated significant superiority over traditional U-Net encoder and histogram analysis methods.