基于二次分解策略和混合模型的光伏发电预测
1College of Mathematics and System Science, Xinjiang University, Urumqi, 830017, China. xueshuyi888@gmail.com.
Scientific reports
|March 11, 2026
概括
准确的超短期光伏电力预测对于电网稳定性至关重要. 本研究引入了使用CEEMDAN和BKA-VMD的混合模型,以提高预测准确度,降低电网运营成本.
科学领域:
- 可再生能源系统可再生能源系统
- 电力系统工程 电力系统工程
- 人工智能在能源中的作用
背景情况:
- 光伏 (PV) 电力的日益集成到电网中,需要精确的超短期预测,以确保电网的稳定性和高效运行.
- 传统的预测方法在短期时间尺度上与光伏功率数据固有的波动性和非线性作斗争.
研究的目的:
- 通过结合先进的信号分解和机器学习技术,开发一个强大的混合框架,用于超短期的光伏电力预测.
- 提高光伏功率预测的准确性和可靠性,以改善电网管理.
主要方法:
- 使用完整的集体实证模式分解与自适应噪声 (CEEMDAN) 进行初始光伏电源系列分解.
- 应用K-means基于样本的聚类来将内在模式函数 (IMF) 分成高频和低频组件.
- 采用黑翼风算法 (BKA) - 变量模式分解 (VMD) 来进一步改进高频组件.
- 杆在线内核极端学习机器 (OKELM) 用于高频子信号预测和卷积神经网络 (CNN) -回声状态网络 (ESN) 用于低频组件建模.
- 通过添加式重建进行综合预测,以获得最终的超短期预测.
主要成果:
- 在比较和验证实验中,高精度的R平方值分别为99.6987%和99.0635%.
- 在不同地理位置 (中国和澳大利亚) 和不同季节表现一致.
- 混合方法有效地处理了光伏功率数据的波动性和非线性.
结论:
- 拟议的混合预测框架显著提高了超短期光伏电力预测的准确性和稳定性.
- 准确的预测提高了电网稳定性,优化了调度和储备调度,并降低了运营成本和光伏电力削减.
- 这种先进的预测方法为管理可再生能源融入现代电网提供了有价值的工具.
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