基于奇异谱分析的Transformer神经网络光伏预测算法  

Transformer Neural Network Photovoltaic Prediction Algorithm Based on Singular Spectrum Analysis

作　　者：韩家鹏 丁蕾蕾 韩崇[2] HAN Jiapeng;DING Leilei;HAN Chong(Hangzhou Branch,Zhejiang Zhongtong Wenbo Service Co.Ltd.,Hangzhou 310013,China;College of Computer,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)

机构地区：[1]浙江中通文博服务有限公司杭州分公司,浙江杭州310013 [2]南京邮电大学计算机学院,江苏南京210023

出　　处：《软件导刊》2025年第3期8-15,共8页Software Guide

基　　金：国家自然科学基金项目(62272242);南京邮电大学校企合作项目(KH0040321038)。

摘　　要：精准的光伏发电预测能为电网安全稳定运行提供保障。针对当前光伏预测算法预处理方法过于简单、周期规律识别效率不高等问题,提出一种基于奇异谱分析的Transformer神经网络光伏预测算法SSA-Trans。该算法在数据处理中引入奇异谱分析技术,在去除太阳辐照度时间序列中影响较大的噪声序列后进行重构,对重构后的序列建立Transformer网络预测模型,将序列的时间戳进行位置编码后与天气数据一同作为网络的特征输入。采用滑动窗口将划分好的数据输入到Transformer模型中进行训练,然后进行预测。在3个公开数据集上对所提算法与对照算法的预测性能进行比较,结果表明,所提算法的归一化平均绝对误差分别降低了约31.94%、20.37%和14.07%。同时,消融实验证实了奇异谱分析和滑动窗口技术的有效性。Accurate photovoltaic power generation prediction can provide guarantees for the safe and stable operation of the power grid.A Transformer neural network photovoltaic prediction algorithm SSA Trans based on singular spectrum analysis is proposed to address the problems of overly simple preprocessing methods and insufficient efficiency in identifying periodic patterns in current photovoltaic prediction algorithms.This algorithm introduces singular spectrum analysis technology in data processing,and reconstructs the sequence after removing the noise sequence that has a significant impact on the solar irradiance time series.A Transformer network prediction model is established for the reconstructed sequence,and the timestamp of the sequence is position encoded and used as the network's feature input along with weather data.Using a sliding window to input the divided data into the Transformer model for training and then for prediction.Comparing the predictive performance of the proposed algorithm with the other existing algorithms on three publicly available datasets,the results showed that the normalized average absolute error of the proposed algorithm decreased by approximately 31.94%,20.37%,and 14.07%,respectively.Meanwhile,the ablation experiment confirmed the effectiveness of singular spectrum analysis and sliding window technique.

关 键 词：光伏预测 奇异谱分析 位置编码 TRANSFORMER 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]