用注意力生成模型提升风电光伏场景预测的不确定性刻画能力
ScenGAN: Attention-Intensive Generative Model for Uncertainty-Aware Renewable Scenario Forecasting
- 融合注意力机制与生成对抗网络，捕捉风光发电时空动态
- 同时建模随机不确定性和认知不确定性，提升预测可信度
- 适合能源系统规划与电力市场交易场景使用
为应对可再生能源出力的间歇性问题，场景预测通过一系列随机实现提供更灵活、直观的预测结果。本文从长时间序列视角出发，研究可再生能源出力与深度学习中的不确定性问题，提出一种面向不确定性感知的可再生能源场景预测模型——ScenGAN。该模型结合注意力机制与生成对抗网络（GAN），精准捕捉复杂的时空动态特征；引入贝叶斯深度学习与自适应实例归一化（AdaIN），模拟典型模式与变异行为，增强不确定性解释性；同时在处理层融合气象信息、预报数据与历史轨迹，提升多尺度周期规律的协同建模能力。数值实验与案例分析表明，所提方法能有效表征包括随机不确定性和认知不确定性在内的双重不确定性，在性能上优于现有先进方法。
原文摘要 · Abstract (English)
To address the intermittency of renewable energy source (RES) generation, scenario forecasting offers a series of stochastic realizations for predictive objects with superior flexibility and direct views. Based on a long time-series perspective, this paper explores uncertainties in the realms of renewable power and deep learning. Then, an uncertainty-aware model is meticulously designed for renewable scenario forecasting, which leverages an attention mechanism and generative adversarial networks (GANs) to precisely capture complex spatial-temporal dynamics. To improve the interpretability of uncertain behavior in RES generation, Bayesian deep learning and adaptive instance normalization (AdaIN) are incorporated to simulate typical patterns and variations. Additionally, the integration of meteorological information, forecasts, and historical trajectories in the processing layer improves the synergistic forecasting capability for multiscale periodic regularities. Numerical experiments and case analyses demonstrate that the proposed approach provides an appropriate interpretation for renewable uncertainty representation, including both aleatoric and epistemic uncertainties, and shows superior performance over state-of-the-art methods.
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