Hybrid Deep Learning for Practical Energy Demand Forecasting: Temperature-Aware Sector-Specific Consumption Prediction with Ensemble Networks and GAN-Enhanced Data

Energy consumption forecasting requires balancing model sophistication with practical deployability, particularly under extreme weather volatility and data scarcity. This paper proposes a two-stage hybrid framework integrating advanced temporal modeling with generative data augmentation.In Stage 1, a Ensemble architecture combines Transformer (for long-range dependencies), LSTM (for sequential dynamics), and TCN (for multi-scale patterns) networks, fused by an XGBoost meta-learner. A key innovation is the dual-pipeline design that simultaneously forecasts daily maximum and minimum temperatures, precisely capturing diurnal thermal amplitude essential for peak load planning.To address extreme-weather sample scarcity, a three-phase GAN framework is introduced, integrating RGANomaly for anomaly detection, a GASN module with Multi-Head Self-Attention for latent temporal modeling, and TAnoGAN to synthesize realistic extreme-temperature scenarios.In Stage 2, these enhanced forecasts drive multivariate consumption models (SARIMAX, VARMAX) across heterogeneous sectors. Results confirm that modeling inter-sector dependencies is critical, reducing Service sector prediction error by 18.6%. The proposed framework achieves a 9.8% reduction in combined forecasting error compared to a robust XGBoost baseline(1024 estimators). Validated on five years of real-world data, the method demonstrates superior robustness in capturing high-volatility events while remaining deployable in resource-constrained utility environments.