对比多种模型，发现TFT在家庭用电量预测中表现最优。
Robust Probabilistic Load Forecasting for a Single Household: A Comparative Study from SARIMA to Transformers on the REFIT Dataset
- 用时序融合变压器（TFT）处理数据缺失问题，提升预测鲁棒性。
- TFT实现最低点预测误差（RMSE 481.94），且区间更安全覆盖极端波动。
- 适合关注家庭用电风险与不确定性的能源系统研究者参考。
概率预测对现代风险管理至关重要，能帮助决策者量化关键系统的不确定性。本文针对波动性强的REFIT家庭用电数据集展开研究，该数据存在显著结构化缺失。首先通过严格对比实验筛选出季节性插补方法，证明其在保留数据分布方面优于线性插值。随后系统评估了从经典模型（SARIMA、Prophet）到机器学习（XGBoost）及深度学习架构（LSTM）的多类模型。结果表明，经典模型无法捕捉数据的非线性与状态切换特征；尽管LSTM提供最校准的概率预测，但时序融合变压器（TFT）整体表现最佳，点预测误差最低（RMSE 481.94），同时生成更保守、更安全的预测区间，有效应对极端波动。
原文摘要 · Abstract (English)
Probabilistic forecasting is essential for modern risk management, allowing decision-makers to quantify uncertainty in critical systems. This paper tackles this challenge using the volatile REFIT household dataset, which is complicated by a large structural data gap. We first address this by conducting a rigorous comparative experiment to select a Seasonal Imputation method, demonstrating its superiority over linear interpolation in preserving the data's underlying distribution. We then systematically evaluate a hierarchy of models, progressing from classical baselines (SARIMA, Prophet) to machine learning (XGBoost) and advanced deep learning architectures (LSTM). Our findings reveal that classical models fail to capture the data's non-linear, regime-switching behavior. While the LSTM provided the most well-calibrated probabilistic forecast, the Temporal Fusion Transformer (TFT) emerged as the superior all-round model, achieving the best point forecast accuracy (RMSE 481.94) and producing safer, more cautious prediction intervals that effectively capture extreme volatility.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。