在复杂的环境条件下进行光伏电力预测的混合TimeGAN-xLSTM-变压器框架
Bingshang Chu1, Junzhuo Shu2, Chenhui Zhao1
1Xuejiawan Power Supply Branch, Inner Mongolia Power (Group) Co., Ltd, Erdos City, Inner Mongolia, China.
Scientific reports
|February 13, 2026
概括
本研究介绍了一个TimeGAN-xLSTM-变压器模型,用于准确的光伏 (PV) 功率预测. 混合方法通过合成数据和捕获复杂的时间依赖性来提高预测准确性.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能在能源中的作用
- 时间序列预测时间序列预测
背景情况:
- 由于光伏能源的日益集成,光伏 (PV) 电力预测至关重要.
- 传统的预测方法与光伏输出的随机性,非线性和异质性作斗争.
- 有限或不平衡的历史数据对光伏功率预测构成了重大挑战.
研究的目的:
- 为准确和概率的光伏电力预测开发一种新的混合框架.
- 通过先进的数据增强来解决稀缺的历史数据集的局限性.
- 改进光伏发电中局部和远程时间依赖性的建模.
主要方法:
- 使用TimeGAN合成现实的光伏时间序列数据,保持辐射-温度关系.
- 开发一种混合架构,将本地特征的扩展LSTM (xLSTM) 和远程依赖的变压器结合起来.
- 通过中国国家电网的现实运营数据验证拟议的模型.
主要成果:
- 时间GAN-xLSTM-变压器框架显著提高了光伏功率预测的准确性.
- 与基线模型相比,根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 降低了约48.1%,降低了44.1%.
- 在捕捉光伏发电的短期波动和长期时间动态方面表现出卓越的能力.
结论:
- 拟议的混合框架为智能光伏预测提供了一个强大的解决方案.
- 利用数据生成,时间建模和注意力机制提高了可靠性.
- 为将光伏电力整合到以可再生能源为主导的电网提供理论和实践支持.
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