摘要
针对海洋极端环境复杂性、空气动力学非线性、海上风电功率的强随机波动性等特点,提出一种融合Kolmogorov-Arnold网络架构的改进小波卷积长短期记忆网络(CKAN-IWTC-LSTM)海上风电功率预测方法。首先,采用引入星型聚合分发模块(STAD)的改进小波卷积(IWTC)特征提取方法,以增强海上多维气象特征的交互表征;其次,构建基于KAN架构强化输出的海上风电LSTM预测模型,挖掘海上风电功率数据时序变化规律;最后,建立多维度评估指标体系,并基于沙普利加性解释(SHAP)方法量化时序特征与环境特征对海上风电功率的贡献度。算例分析表明,所提方法能够有效实现海上风电功率的精准预测。
In response to the complex marine extreme environments,nonlinear aerodynamic effects,and strong stochastic fluctuations of offshore wind power,this paper proposes an offshore wind power forecasting method that integrates an improved wavelet convolutional long short-term memory network with the Kolmogorov-Arnold Network architecture(CKAN-IWTC-LSTM).First,an improved wavelet convolution(IWTC)feature extraction method incorporating a star aggregation and distribution module(STAD)is adopted to enhance the interactive representation of multidimensional meteorological features offshore.Second,an LSTM forecasting model for offshore wind power with enhanced output based on the KAN architecture is constructed to capture the temporal variation patterns in offshore wind power data.Finally,a multi-dimensional evaluation framework is established,and the Shapley additive exPlanations(SHAP)method is employed to quantify the contributions of temporal and environmental features to offshore wind power.Case study analysis demonstrates that the proposed method effectively achieves accurate forecasting of offshore wind power.
作者
田书欣
姜皓喆
秦世耀
符杨
杨喜军
李振坤
TIAN Shuxin;JIANG Haozhe;QIN Shiyao;FU Yang;YANG Xijun;LI Zhenkun(Engineering Research Center of Offshore Wind Technology,Ministry of Education(Shanghai University of Electric Power),Shanghai 200090,China;School of Electrical Engineering,Shanghai University of Electric Power,Shanghai 200090,China;State Key Laboratory of Renewable Energy Grid-Integration(China Electric Power Research Institute),Beijing 100192,China;Department of Electrical Engineering,Shanghai Jiaotong University,Shanghai 200240,China)
出处
《智慧电力》
北大核心
2025年第11期91-98,共8页
Smart Power
基金
国家重点研发计划资助项目(2022YFB2402800)。
