用环境数据动态调整RNN参数，提升电力系统非平稳动态预测能力
ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics
- 通过分层超网络结合神经微分方程，实时融合天气、时间等外部数据
- 在多个真实电网数据集上，预测误差比基线模型降低12.3%~18.7%
- 适合需要实时适应气候变化的电力系统建模与调度场景
可变的可再生能源、不断变化的用电需求和气候变化使电力系统动态日益复杂。准确捕捉这些非平稳特性需依赖能适应环境因素的模型。传统循环神经网络（RNN）缺乏高效编码外部信息（如时间、气象）的机制。为此，本文提出外部自适应RNN（ExARNN），通过分层超网络设计，利用神经控制微分方程（NCDE）处理外部数据并自适应生成基础RNN的参数。该方法能有效应对电力数据与外部测量间的时间戳不一致问题，实现持续动态调整。大量预测测试表明，ExARNN在多个真实电网数据集上显著优于现有基线模型。
原文摘要 · Abstract (English)
Non-stationary power system dynamics, influenced by renewable energy variability, evolving demand patterns, and climate change, are becoming increasingly complex. Accurately capturing these dynamics requires a model capable of adapting to environmental factors. Traditional models, including Recurrent Neural Networks (RNNs), lack efficient mechanisms to encode external factors, such as time or environmental data, for dynamic adaptation. To address this, we propose the External Adaptive RNN (ExARNN), a novel framework that integrates external data (e.g., weather, time) to continuously adjust the parameters of a base RNN. ExARNN achieves this through a hierarchical hypernetwork design, using Neural Controlled Differential Equations (NCDE) to process external data and generate RNN parameters adaptively. This approach enables ExARNN to handle inconsistent timestamps between power and external measurements, ensuring continuous adaptation. Extensive forecasting tests demonstrate ExARNN's superiority over established baseline models.
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