提出多尺度电力负荷预测框架，提升中长期依赖捕捉能力。
A Unified Energy Management Framework for Multi-Timescale Forecasting in Smart Grids
- 设计含时序位置编码的新架构，建模跨时间尺度依赖
- 在真实数据上优于多个强基线模型，提升预测精度
- 适合智能电网调度与需求响应场景的从业者参考
准确预测电力负荷，如峰值功率的大小和出现时间，对成功管理电力系统及实施智能电网策略（如需求响应和削峰）至关重要。在多时间尺度优化调度中，滚动优化是常用方法，但需考虑不同时间尺度间优化目标的耦合性。准确捕捉时间序列数据中的中长期依赖关系具有挑战性。本文提出 Multi-pofo 框架，通过新颖的架构配置时序位置编码层，有效捕捉此类依赖。为验证所提模型的有效性，我们在真实世界电力负荷数据上进行了实验。结果表明，该方法在多个指标上均优于多种强基线模型。
原文摘要 · Abstract (English)
Accurate forecasting of the electrical load, such as the magnitude and the timing of peak power, is crucial to successful power system management and implementation of smart grid strategies like demand response and peak shaving. In multi-time-scale optimization scheduling, rolling optimization is a common solution. However, rolling optimization needs to consider the coupling of different optimization objectives across time scales. It is challenging to accurately capture the mid- and long-term dependencies in time series data. This paper proposes Multi-pofo, a multi-scale power load forecasting framework, that captures such dependency via a novel architecture equipped with a temporal positional encoding layer. To validate the effectiveness of the proposed model, we conduct experiments on real-world electricity load data. The experimental results show that our approach outperforms compared to several strong baseline methods.
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