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English(EN) Robustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically Interpretable Analysis

深度学习模型在光伏发电预测中对天气预报误差表现出韧性

作者 PulseAugur 编辑部 · [2 个来源] ·

一项新研究评估了包括PatchTST、GRU、N-HITS和LightGBM在内的各种深度学习模型在数值天气预报（NWP）数据存在误差时对这些误差的鲁棒性。该研究引入了一个物理约束框架来模拟这些误差并评估它们对光伏（PV）发电预测的影响。研究结果表明，与表格模型相比，序列模型在噪声过滤和时间鲁棒性方面表现更优，尤其是在显著的扰动水平下。SHAP和Integrated Gradients等技术揭示，模型可以将其预测依赖从损坏的未来预测转移到更稳定的历史数据和物理先验。 AI

影响 增强了对人工智能模型在现实、不确定条件下可靠性的理解，指导了关键预测任务的模型选择。

排序理由 该集群包含一篇学术论文，详细介绍了一个新的评估框架和对现有模型的分析。

在 arXiv cs.LG 阅读 →

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深度学习模型在光伏发电预测中对天气预报误差表现出韧性

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该集群包含一篇学术论文，详细介绍了一个新的评估框架和对现有模型的分析。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Dandan Chen, Yan Zhao, Xuepeng Chen ·

    NWP预测误差下光伏发电预测深度学习模型的鲁棒性：时空和物理可解释性分析

    arXiv:2607.12954v1 Announce Type: cross Abstract: Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs. In photovoltaic (PV) forecasting, this requirement is especially challenging because numerical …

  2. arXiv cs.LG TIER_1 English(EN) · Xuepeng Chen ·

    用于光伏发电预测的深度学习模型在数值天气预报误差下的鲁棒性：时空和物理可解释性分析

    Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs. In photovoltaic (PV) forecasting, this requirement is especially challenging because numerical weather prediction (NWP) errors are temporally cor…

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