标题： 基于评分的条件扩散模型的海上风电场集群台风条件概率预测方法 显示英文标题

标题： Probabilistic Forecasting Method for Offshore Wind Farm Cluster under Typhoon Conditions: a Score-Based Conditional Diffusion Model

摘要： 在台风条件下，海上风电（OWP）表现出显著的波动，这对电力系统的安全运行构成了重大挑战。因此，准确预测OWP至关重要。然而，历史台风数据的固有稀缺性和OWP的随机性使得传统点预测方法尤其困难且不足。为解决这一挑战，并为电网运营商提供决策所需的全面信息，本研究提出了一种基于评分的条件扩散模型（SCDM），用于台风期间OWP的概率预测。首先，采用知识图算法将历史台风路径嵌入为向量。然后，构建一个确定性网络，基于这些向量嵌入预测台风条件下的风力发电量。最后，为更好地表征预测误差，开发了一个去噪网络。该方法的核心是一种均值回归随机微分方程（SDE），它将复杂的误差分布转换为标准高斯分布，从而能够使用反向时间SDE对预测误差进行采样。通过将确定性预测与采样的误差相结合，重建概率预测结果。所提出的方法使用一组9个海上风电场的真实数据进行评估。结果表明，在台风条件下，我们的方法在确定性和概率指标上均优于基线模型，验证了该方法的有效性。 显示英文摘要

摘要： Offshore wind power (OWP) exhibits significant fluctuations under typhoon conditions, posing substantial challenges to the secure operation of power systems. Accurate forecasting of OWP is therefore essential. However, the inherent scarcity of historical typhoon data and stochasticity of OWP render traditional point forecasting methods particularly difficult and inadequate. To address this challenge and provide grid operators with the comprehensive information necessary for decision-making, this study proposes a score-based conditional diffusion model (SCDM) for probabilistic forecasting of OWP during typhoon events. First, a knowledge graph algorithm is employed to embed historical typhoon paths as vectors. Then, a deterministic network is constructed to predict the wind power under typhoon conditions based on these vector embeddings. Finally, to better characterize prediction errors, a denoising network is developed. At the core of this approach is a mean-reverting stochastic differential equation (SDE), which transforms complex error distributions into a standard Gaussian, enabling the sampling of forecasting errors using a reverse-time SDE. The probabilistic forecasting results are reconstructed by combining deterministic forecasts with sampled errors. The proposed method is evaluated using real-world data from a cluster of 9 offshore wind farms. Results demonstrate that under typhoon conditions, our approach outperforms baseline models for both deterministic and probabilistic metrics, verifying the effectiveness of the approach.