基于TimeGAN的极端天气光伏功率预测方法

doi:10.3969/j.issn.2097-0706.2025.09.006

摘要/Abstract

摘要：

极端天气下准确预测光伏发电量对保障能源供应和电网稳定至关重要，但此类天气的突发性导致光伏电站历史数据稀缺，难以有效预测极端天气场景下的光伏功率。针对上述问题，提出一种基于时间序列生成对抗网络（TimeGAN）的少量历史数据扩充预测方法，捕捉光伏功率和天气条件中的复杂时间依赖关系，根据光伏电站已有少量历史数据，生成逼真的时间序列数据，模拟极端天气发生的过程，进而展开光伏功率预测。试验结果显示，相较于采用传统生成对抗网络（GAN）扩增小样本数据，采用TimeGAN扩充小样本后的预测结果有较好的拟合性，数据扩增25%后平均绝对误差（MAE）降低了1.14 MW，均方根误差（RMSE）降低了1.09 MW，数据扩增50%后MAE降低了1.08 MW，RMSE降低了0.99 MW，精确度得到了明显提高。

关键词: TimeGAN, 极端天气, 小样本扩充, 光伏功率预测, 时间序列

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.

Key words: TimeGAN, extreme weather, small sample augmentation, photovoltaic power prediction, time series

中图分类号:

TK51：TM761

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.

SUN Shiqi, MA Gang, XU Wenjun, LI Hao, MA Jian. TimeGAN-based photovoltaic power prediction method under extreme weather events[J]. Integrated Intelligent Energy, 2025, 47(9): 51-59.

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区间	云量	相对湿度	环境温度	风速	气压	辐照度
0.1	0.312 4	0.225 2	0.344 3	0.381 4	0.183 1	0.684 2
0.2	0.363 3	0.258 1	0.471 4	0.502 1	0.208 7	0.736 4
0.3	0.495 2	0.287 4	0.572 3	0.592 1	0.216 4	0.771 9
0.4	0.567 1	0.291 5	0.593 6	0.645 2	0.263 2	0.819 2
0.5	0.600 4	0.345 3	0.645 3	0.697 2	0.298 8	0.832 3
0.6	0.628 4	0.378 9	0.691 4	0.739 3	0.313 1	0.858 2
0.7	0.661 1	0.482 1	0.725 6	0.754 5	0.328 4	0.890 1
0.8	0.699 8	0.531 4	0.748 4	0.788 2	0.357 9	0.901 3
0.9	0.725 3	0.559 0	0.761 2	0.791 7	0.389 1	0.910 4
1.0	0.746 7	0.574 1	0.777 5	0.813 1	0.393 5	0.923 7

模型	参数	数值
TimeGAN	θ	2
	λ	0.1
	逻辑框架数量	3
	学习率1	0.001
	学习率2	0.005
	批量大小	56
CNN-LSTM	学习率	0.009
	隐藏层	134
	迭代次数	100
	批量大小	128
	梯度阈值	1

数据量	MAE/MW	RMSE/MW	R²
无扩增	7.82	10.96	0.763
扩增25%	5.40	5.81	0.873
扩增50%	4.09	4.98	0.891

数据扩增	MAE/MW	RMSE/MW	R²
无扩增	7.15	10.60	0.796
25%	4.26	4.72	0.878
50%	3.11	3.99	0.912

数据扩增方式	准确度	精度	召回率
GAN	96.09±0.79	92.93±2.76	74.75±0.63
TimeGAN	98.16±0.27	98.93±0.46	66.63±1.50