软件导刊2025，Vol.24Issue(3)：8-15,8.DOI:10.11907/rjdk.242004
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
摘要
Abstract
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 prob-lems of overly simple preprocessing methods and insufficient efficiency in identifying periodic patterns in current photovoltaic prediction algo-rithms.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 da-ta.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 nor-malized average absolute error of the proposed algorithm decreased by approximately 31.94%,20.37%,and 14.07%,respectively.Mean-while,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/TransformerKey words
photovoltaic forecasting/singular spectrum analysis/position encoding/Transformer分类
计算机与自动化引用本文复制引用
韩家鹏,丁蕾蕾,韩崇..基于奇异谱分析的Transformer神经网络光伏预测算法[J].软件导刊,2025,24(3):8-15,8.基金项目
国家自然科学基金项目(62272242) （62272242）
南京邮电大学校企合作项目(KH0040321038) （KH0040321038）
