用可变方差非平稳高斯过程提升风电功率预测精度
Probabilistic Wind Power Modelling via Heteroscedastic Non-Stationary Gaussian Processes
- 基于广义谱混合核构建可变方差的非平稳高斯过程
- 在10分钟级SCADA数据上显著优于传统模型和基线方法
- 适合需要精准不确定性建模的风电并网系统
准确的风电功率概率预测对维持电网稳定和高效集成可再生能源至关重要。高斯过程（GP）模型能提供不确定性量化，但传统方法通常依赖平稳核函数和同方差噪声假设，难以捕捉风速与功率输出固有的非平稳性和异方差性。本文提出一种基于广义谱混合核的异方差非平稳高斯过程框架，能够建模输入相关的相关性及输入相关的变异性。我们在10分钟级监控与数据采集（SCADA）数据上评估该模型，并与具有平稳和非平稳核的GP变体以及常用非GP概率基线进行对比。结果表明，在风电功率预测中同时建模非平稳性和异方差性至关重要，且灵活的非平稳GP模型在实际SCADA场景中具有显著应用价值。
原文摘要 · Abstract (English)
Accurate probabilistic prediction of wind power is crucial for maintaining grid stability and facilitating the efficient integration of renewable energy sources. Gaussian process (GP) models offer a principled framework for quantifying uncertainty; however, conventional approaches typically rely on stationary kernels and homoscedastic noise assumptions, which are inadequate for modelling the inherently non-stationary and heteroscedastic nature of wind speed and power output. We propose a heteroscedastic non-stationary GP framework based on the generalised spectral mixture kernel, enabling the model to capture input-dependent correlations as well as input-dependent variability in wind speed-power data. We evaluate the proposed model on 10-minute supervisory control and data acquisition (SCADA) measurements and compare it against GP variants with stationary and non-stationary kernels, as well as commonly used non-GP probabilistic baselines. The results highlight the necessity of modelling both non-stationarity and heteroscedasticity in wind power prediction and demonstrate the practical value of flexible non-stationary GP models in operational SCADA settings.
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