对光伏发电采用双域季节性混合预测策略,考虑到动态的不确定波动
Zhaowei Yuan1,2, Yaosong Xu1, Sen Xie3
1Institute of Electrical and Control Engineering, Liaoning Technical University, Huludao, 125100, China.
Scientific reports
|November 21, 2025
概括
准确的光伏 (PV) 功率预测对于太阳能利用至关重要. 本研究提出了一种双域季节性混合战略,使用先进的神经网络来提高光伏功率预测的准确性和稳定性.
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:
- 光伏 (PV) 输出功率的不可预测性挑战了实时需求匹配.
- 准确的太阳能预测对于高效的电网整合和利用至关重要.
- 光伏发电的间歇性和不确定性需要先进的预测方法.
研究的目的:
- 为光伏发电提出一种新的双域季节性混合预测策略.
- 为了提高光伏功率输出预测的准确性和稳定性.
- 为应对太阳能固有的不确定性和间歇性所带来的挑战.
主要方法:
- 光伏功率数据的季节性分类.
- 自相对应分析以区分高动态特征和低动态特征.
- 扩展受体场卷积神经网络 (ERCNN) 的开发,用于特征提取.
- 在不同波动类型的双向长短期存储器 (BiLSTM) 网络中整合多头注意力和修改门的层次架构.
- 时间卷积网络 (TCN) 用于剩余错误的纠正.
主要成果:
- 与现有模型相比,拟议的混合模型显示出更高的预测准确性.
- 该策略有效地处理光伏功率数据的不频繁和频繁波动.
- 对真实光伏数据集的验证证实了该模型的稳定性和有效性.
- ERCNN,MIOGBiLSTM注意力,MOGBiLSTM注意力和TCN组件有助于提高预测性能.
结论:
- 双域季节性混合预测策略在光伏功率预测方面取得了重大进展.
- 集成先进的深度学习技术提高了太阳能预测的可靠性.
- 这种方法为管理可再生能源的变化提供了一个强大的解决方案.
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