Uncertainty-Aware Temporal Forecasting for Community Microgrid Demand Response

The manuscript proposes an uncertainty-aware forecasting framework for microgrid demand response, characterized by exceptionally high-quality technical writing and logical organization. However, the council reached a consensus that the paper suffers from a 'terminal empirical gap': the primary results for the proposed UA-TFN model are systematically missing from the key performance tables, rendering the core claims unverifiable. Furthermore, the experimental design lacks critical ablation studies to isolate the benefits of the adaptive reserve logic from the underlying forecasting architecture, and the reliance on a single synthetic dataset without cross-validation undermines the broad claims of generalizability. While the integration of quantile forecasting is conceptually sound, the current lack of quantitative evidence and statistical significance testing necessitates a major revision. The authors must provide complete comparative data, isolate variables through ablation, and validate their model against real-world or multi-site benchmarks to meet the standards for publication.