Heterogeneity-aware model-agnostic meta-learning for residential building energy prediction

Accurate residential energy forecasting is critical for smart grid operation and HVAC efficiency, yet it is inherently constrained by severe data scarcity and complex user heterogeneity. Standard meta-learning approaches often suffer from statistical distribution bias, as the meta-gradient typically overfits to dominant consumption patterns while neglecting minority households. To overcome this limitation, this study proposes a heterogeneity-aware model-agnostic meta-learning framework driven by a deep generative pre-categorization strategy (Cat-MAML). Abandoning heuristic statistical clustering, our approach utilizes a 1D-convolutional time-series variational autoencoder (TS-VAE) and Gaussian mixture model (GMM) soft-clustering to project raw consumption profiles into a continuous latent space, extracting profound behavioral archetypes. By enforcing a balanced task sampling distribution derived from this latent space during meta-training, the framework ensures equitable representation of both dominant and rare user groups. Experiments on a diverse, cross-climatic building energy dataset demonstrate that the proposed heterogeneity-aware framework significantly outperforms both a global baseline and vanilla MAML, achieving a 32.7% reduction in average mean squared error (MSE) and a remarkable 61.5% error reduction specifically for extreme out-of-distribution (OOD) adversarial stress-test cases. Furthermore, under simulated non-stationary online deployment scenarios, the framework exhibits superior long-term robustness and rapid adaptation efficiency, highlighting its immense practical value for scalable, data-limited residential energy management.