开发CC-TF-BiGRU模型,以提高光伏发电预测的准确性
Guomin Xie1, Zijian Zhang1, Zhongbao Lin1
1Institute of Electrical and Control Engineering, Liaoning Technical University, Huludao, 125100, China.
Scientific reports
|April 21, 2025
概括
本研究提出了一种新的短期光伏 (PV) 功率预测方法,使用教师强制 (TF) 与双向封闭循环单位 (BiGRU). 这种方法提高了清洁能源解决方案的准确性和稳定性.
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:
- 全球日益增长的能源需求和环境问题需要可持续的能源解决方案.
- 光伏发电对于向清洁能源未来过渡至关重要.
- 准确的短期光伏电力预测对于电网稳定性和能源管理至关重要.
研究的目的:
- 引入一种新的短期光伏电力预测方法.
- 为了提高光伏功率预测的准确性和稳定性.
- 为光伏预测提供一个强大的技术和理论框架.
主要方法:
- 混沌特征提取和C-C方法来识别关键的光伏功率动态因素.
- 整合太阳辐射数据以进行增强的预测.
- 一个混合模型,结合了梯度增强决策树 (GBDT) 和双向封闭循环单元 (BiGRU) 进行时间序列数据处理.
- 实施教师强制 (TF) 以提高模型性能.
主要成果:
- 拟议的方法论在预测光伏功率方面表现出很高的表现.
- 该模型在多样化和复杂的天气条件下证明有效.
- 实验验证证证了TF-BiGRU方法的准确性和稳定性.
结论:
- 开发的方法为短期光伏电力预测提供了一种开创性的方法.
- 这项研究为可再生能源行业提供了宝贵的理论和技术框架.
- 这些发现支持集成先进的人工智能技术,以优化光伏发电.
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