电网技术2024，Vol.48Issue(4)：1455-1465,中插22-中插24,14.DOI:10.13335/j.1000-3673.pst.2023.1785
融合精细化气象因素与物理约束的深度学习模型在短期风电功率预测中的应用
Application of Deep Learning Model Integrating Refined Meteorological Factors and Physical Constraints in Short-term Wind Power Prediction
摘要
Abstract
Existing wind power prediction based on deep learning methods is an indirect prediction using meteorological data as input,and its prediction accuracy depends on the accuracy of meteorological forecasts.However,existing meteorological forecast data generally suffer from low-resolution and unstable models.At the same time,deep learning models are completely data-driven and need guidance from physical laws,making it difficult to improve prediction accuracy further.Therefore,this paper proposes a method that combines refined meteorological factors with physical deep learning.Firstly,numerical weather prediction data is processed using downscaling and multi-model integration techniques to improve meteorological forecast products'low resolution and accuracy issues.Secondly,two physical models are introduced based on wind turbine wake effects and power curves.On the one hand,the physical models are embedded in the neural network loss function as regularization terms to introduce physical constraints in the learning process and construct a physical deep learning network.On the other hand,the physical models are used to generate pre-training samples to address the insufficient observational data,obtaining a pre-training model that supports subsequent supervised learning tasks.Finally,the effectiveness and superiority of the proposed method are verified through simulation analysis of actual data from a coastal wind farm in a certain city.关键词
风电功率/数值天气预报/降尺度/多模式集成/物理深度学习Key words
wind power/numerical weather prediction/downscaling/multi-model integration/physical deep learning分类
信息技术与安全科学引用本文复制引用
邬永,王冰,陈玉全,姜华..融合精细化气象因素与物理约束的深度学习模型在短期风电功率预测中的应用[J].电网技术,2024,48(4):1455-1465,中插22-中插24,14.基金项目
国家自然科学基金项目(62303158).Project Supported by National Natural Science Foundation of China(62303158). （62303158）
