Hypergraph neural network and temporal convolutional network for load forecasting in smart construction sites

Abstract

This study proposes a novel Time series Decomposition and Attention Graph Neural Network (TDAGNN) that integrates temporal decomposition and attention mechanisms to address the challenges of complex spatiotemporal coupling and abrupt phase transitions in load forecasting for high-rise building construction. A Dual Time series Decomposition Convolutional Neural Network (DTDCNN) is employed to extract temporally dependent features from intricate high-altitude construction load data. To effectively capture the heterogeneous and dynamic characteristics of load fluctuations, a Multi-head Interactive Attention (MIA) module is introduced, enabling interactive learning between original and locally enhanced features. Furthermore, a Self-scaling Dynamic Diffusion Graph Neural Network (SDDGNN) is incorporated to model spatial dependencies while mitigating scale distortion commonly encountered in graph-based methods. Experimental evaluations on the expanded BIM-SHMC dataset demonstrate that the proposed framework achieves state-of-the-art performance with peak load error rate (PPER) of 8.72 ± 0.23%, power fluctuation matching degree (PFMD) of 92.17 ± 0.41%, resource scheduling alignment (RSA) of 94.32 ± 0.38%, and phase transition detection accuracy (PHA) of 91.25 ± 0.35%, representing an average improvement of 13.3% over the next-best model (STGCN).