基于联邦学习的海上分布式光伏超短期功率预测  

Ultra-short-term power prediction of offshore distributed PV based on federated learning

作　　者：王迎春[1] 王志硕 刘洋[1] 杨东升[1] 解相朋 WANG Ying-chun;WANG Zhi-shuo;LIU Yang;YANG Dong-sheng;XIE Xiang-peng(College of Information Science and Engineering,Northeastern University,Shenyang 110819,China;Internet of Things Academy,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)

机构地区：[1]东北大学信息科学与工程学院,沈阳110819 [2]南京邮电大学物联网学院,南京210003

出　　处：《控制与决策》2025年第2期441-450,共10页Control and Decision

基　　金：国家自然科学基金项目(62173080);国家自然科学基金重点项目(U22B20115)。

摘　　要：功率预测是实现电能供需平衡、维持电网稳定运行的一项重要任务.随着分布式海上光伏系统的发展,光伏利用率不断提升,同时对光伏功率预测提出了更高的要求.针对机器学习方法在光伏功率时间序列预测中存在的样本数量不足、预测精度低以及隐私泄露等问题,提出一种基于联邦学习和变分模态分解的长短期记忆神经网络功率预测模型(long short-term memory neural network power forecasting model based on federated learning and variational mode decomposition,FL-VMD-LSTM).利用主成分分析法和三次样条插值对气象数据进行预处理,同时利用VMD将光伏功率时间序列分解为多个分量进行分步预测,降低光伏功率时间序列的非平稳性和复杂度.通过横向联邦学习的本地训练和参数聚合方法,实现在保证数据隐私安全情况下的光伏功率预测.通过4个算例进行仿真实验,验证结果表明FL-VMD-LSTM模型在光伏功率预测方面具有较高精度,与传统算法相比,RMSE和MAE分别降低了55.7%和55.5%.Power forecasting is an important task for achieving energy supply and demand balance and maintaining stable operation of power grids.With the development of distributed offshore photovoltaic systems,the utilization rate of photovoltaic power is constantly improving,while higher requirements are placed on photovoltaic power forecasting.In view of the problems of insufficient sample size,low prediction accuracy,and privacy leakage in the machine learning method for photovoltaic power time series forecasting,this paper proposes a long short-term memory neural network power forecasting model based on federated learning and variational mode decomposition(FL-VMD-LSTM).The meteorological data is preprocessed by principal component analysis and cubic spline interpolation,and the photovoltaic power time series is decomposed into multiple components for stepwise prediction to reduce the non-stationarity and complexity of the photovoltaic power time series.The local training and parameter aggregation method of horizontal federated learning is used to achieve photovoltaic power forecasting under the condition of data privacy security.Four simulation experiments are conducted to verify that the FL-VMD-LSTM model has high accuracy in photovoltaic power forecasting,with RMSE and MAE reduced by 55.7%and 55.5%,respectively,compared with traditional algorithms.

关 键 词：变分模态分解 主成分分析 功率预测 长短期记忆网络 联邦学习 海上光伏 

分 类 号：TM615[电气工程—电力系统及自动化]