In the global pursuit of carbon neutrality, the manufacturing industry is under increasing pressure to reduce energy waste. Excess consumption not only depletes resources but also hinders sustainable development. Accurate energy consumption prediction is therefore essential for scientific production scheduling and resource allocation, enabling loss reduction, efficiency improvement, and environmental performance enhancement. However, the complexity of modern manufacturing environments results in energy consumption data that is high-dimensional, noisy, and strongly spatiotemporal, which poses challenges to traditional prediction methods. To address these issues, this paper constructs an energy consumption behavior model considering key factors such as equipment status, processing techniques, and environmental conditions. A comprehensive feature analysis and data preprocessing are carried out to identify the key factors influencing consumption. Based on this, an optimization model is proposed that integrates an improved Variational Autoencoder (VAE) with an enhanced Graph Attention Network (GAT). VAE extracts compact latent representations from high-dimensional noisy inputs, suppressing redundancy while preserving essential patterns. GAT then captures complex spatiotemporal dependencies among energy-related features, thereby revealing intrinsic consumption dynamics. Experimental evaluations on both public and real-world datasets demonstrate that the proposed VAE-GAT model achieves superior prediction accuracy and generalization compared with other deep learning baselines. This approach provides a reliable foundation for energy management and contributes to advancing green intelligent manufacturing.