发表机构
Karlsruhe Institute of Technology; Helmholtz AI（卡尔斯鲁厄理工学院; 亥姆霍兹人工智能中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出跨电网层级负荷预测综合基准，评估十种方法，发现基于变压器的方法表现优，误差降6.6 - 10.7%。引入YAformer分析架构影响，标准变压器性能更优。评估Chronos - 2，揭示模型优缺点，强调长输入等因素重要性。
AI 中文摘要
准确的多电网层级负荷预测对未来智能电网至关重要，涵盖从供需平衡的聚合控制区预测到需求侧管理和能源管理系统的个体终端用户负荷预测。我们提出了一个跨电网层级负荷预测的综合基准，包含三个代表输电系统运营商控制区、低压电网馈线和个体终端用户的数据集。我们评估了十种短期负荷预测方法，发现基于变压器的方法始终优于现有方法，预测误差降低了6.6 - 10.7%。为分析架构设计的影响，我们引入了YAformer，一种灵活的变压器架构，它整合了先前工作的修改并通过超参数优化进行了优化。然而，标准变压器表现更优，表明准确的负荷预测不需要这些架构修改。我们还评估了基于变压器的时序基础模型Chronos - 2，它在两个数据集上展示了有竞争力的零样本性能，但未能准确捕捉输电系统运营商数据中的特殊事件。详细分析揭示了模型特定的优势和劣势，消融研究突出了长输入上下文、协变量和持续再训练的重要性，这些方面在时序预测文献中常被忽视。
英文摘要
Accurate load forecasting at multiple grid levels is essential for future smart grids, ranging from aggregated control area forecasts for balancing supply and demand to forecasts of individual end-consumer loads for demand-side management and energy management systems. We present a comprehensive benchmark for load forecasting across grid levels, comprising three datasets that represent a transmission system operator control area, low-voltage grid feeders, and individual end consumers. We evaluate ten methods for short-term load forecasting and find that Transformer-based approaches consistently outperform established methods, reducing forecast error by 6.6-10.7 %. To analyze the impact of architectural design, we introduce YAformer, a flexible Transformer architecture that integrates modifications from prior work and is optimized via hyperparameter optimization. However, the standard Transformer achieves superior performance, suggesting that these architectural modifications are not required for accurate load forecasting. We further evaluate the Transformer-based time-series foundation model Chronos-2, which demonstrates competitive zero-shot performance on two datasets but fails to accurately capture special events in the TSO data. Detailed analyses reveal model-specific strengths and weaknesses, and ablation studies highlight the importance of long input contexts, covariates and continuous retraining - aspects that are often overlooked in the time-series forecasting literature.
