这篇论文教你怎么选负荷预测模型——TFT内部做分位数学习比事后校正靠谱，区间窄5倍，适合做精确调度。
该论文提出一个统一的一日前概率预测框架，用于比较模块化事后残差分位数与集成模型内分位数学习两种方案。使用三种深度学习骨干网络（循环、混合循环、Temporal Fusion Transformer）在相同输入下进行评估。结果表明TFT上的集成分位数学习性能最优，MAPE为2.2-3.6%，RMSE为28-83W，且区间宽度比模块化方案窄约5倍。重建敏感性测试显示重建输入使分位数得分增加106%，但区间宽度几乎不变，说明模型未自动吸收重建不确定性。该研究揭示事后残差分位数在依赖重建输入时的局限性，并通过非DL基线和季节性验证支持排名。
Learning-based Probabilistic Load Forecasting with Post-hoc and In-model Uncertainty
Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs. Missing features must then be reconstructed, and their errors can propagate through the model. If this input uncertainty is not reflected, prediction intervals may become miscalibrated, affecting demand-response scheduling. Our work examines where uncertainty should be placed once inference inputs are reconstructed. We develop a unified one-day-ahead probabilistic forecasting framework that aligns temporal resolution, reconstructs the unavailable inputs, and derives causal features, and we compare a modular post-hoc residual-quantile scheme with an integrated in-model quantile-learning scheme. The comparison uses three mid-scale Deep Learning (DL) backbones: recurrent, hybrid recurrent, and attention-based Temporal Fusion Transformer (TFT) models, under identical inputs, forecasting horizon, preprocessing rules, and training budgets. Results show that uncertainty placement is backbone-dependent. Integrated quantile learning is most reliable with the TFT, yielding 2.2-3.6% MAPE and 28-83W RMSE on the labeled test window, while producing intervals about 5x narrower than the modular intervals at the closest-to-nominal coverage level. Diebold-Mariano tests support the TFT ranking and the mixed behavior of the recurrent backbones. A reconstruction-sensitivity test shows that reconstructed inputs increase the Quantile Score (QS) by 106% while interval width remains nearly unchanged, indicating that the model does not automatically absorb reconstruction-induced uncertainty. Robustness checks against non-DL baselines and seasonal hold-out weeks support this ranking. Our results expose the limits of post-hoc residual quantiles when inference depends on reconstructed inputs.