综合智慧能源2025，Vol.47Issue(9)：51-59,9.DOI:10.3969/j.issn.2097-0706.2025.09.006
基于TimeGAN的极端天气光伏功率预测方法
TimeGAN-based photovoltaic power prediction method under extreme weather events
摘要
Abstract
Accurate prediction of photovoltaic power generation under extreme weather events is crucial for ensuring energy supply and grid stability.However,the suddenness of such weather events leads to scarce historical data from photovoltaic power stations,making it difficult to effectively predict photovoltaic power under extreme weather conditions.To address this issue,a prediction method based on Time-series Generative Adversarial Networks(TimeGAN)was proposed to augment limited historical data.The method captured the complex temporal dependencies between photovoltaic power and weather conditions.Based on the limited historical data from photovoltaic power stations,the TimeGAN model generated realistic time-series data to simulate the occurrence of extreme weather events,and subsequently conducted photovoltaic power prediction.The experimental results showed that compared to traditional GAN for small sample augmentation,the TimeGAN-augmented prediction results demonstrated better fitting performance.After 25%data augmentation,the Mean Absolute Error(MAE)decreased by 1.14 MW,and the Root Mean Square Error(RMSE)decreased by 1.09 MW.After 50%data augmentation,the MAE decreased by 1.08 MW,and the RMSE decreased by 0.99 MW.These results indicated significant improvements in prediction accuracy.关键词
TimeGAN/极端天气/小样本扩充/光伏功率预测/时间序列Key words
TimeGAN/extreme weather/small sample augmentation/photovoltaic power prediction/time series分类
能源科技引用本文复制引用
孙师奇,马刚,许文俊,李豪,马健..基于TimeGAN的极端天气光伏功率预测方法[J].综合智慧能源,2025,47(9):51-59,9.基金项目
江苏省碳达峰碳中和科技创新专项资金重点项目(BE2022003)Jiangsu Province Carbon Peak and Carbon Neutrality Science and Technology Innovation Special Fund Project(BE2022003) （BE2022003）
