Novel STAttention GraphWaveNet model for residential household appliance prediction and energy structure optimization

Abstract

The multifaceted challenge of carbon dioxide (CO2) emissions during building operations, construction, and material production perpetuates energy shortages and climate adversities. Urgent imperatives underscore the need for effective energy reduction and carbon mitigation, driving the building sector toward a paradigm shift in clean, low-carbon evolution. However, the complex multidimensional and nonlinear characteristics of influencing factors of building energy consumption make it difficult in precisely understanding the spatial and temporal relationships between the features in the predictive modeling of buildings. Therefore, a novel Graph Wavenet (GWN) based on the spatio-temporal attention fusion mechanism (STA-GWN) is proposed for spatio-temporal graph prediction modeling of structures, which can capture hidden spatial relationships using an adaptive dependency matrix. To overcome the drawbacks of standard predefined adjacency matrices and their inability to capture faraway dependencies, a spatio-temporal attention fusion mechanism is introduced to extract latent spatio-temporal features dynamically. By combining extracted features, building energy consumption can be predicted more precisely. Moreover, the proposed model becomes more interpretable, making it easier to reveal how spatio-temporal features relate to building energy consumption. Lastly, the proposed method is applied to energy conservation and emission reduction in the building industry. A predictive model for electrical energy consumption during two public datasets with an operational phase of buildings is established to analyze and optimize energy utilization in residential structures that rely on electricity. The experimental results demonstrate that the STA-GWN model surpasses other deep learning methods in terms of prediction accuracy and robustness. Moreover, the proposed model can offer optimal solutions for building energy processes, reducing approximately 201.44 kg (Kg) and 64.08 Kg of CO2 emissions in the appliances energy prediction dataset and the individual-household-electric-power-consumption (IHEPC) dataset, respectively.