一个新的混合模型用于光伏输出功率预测
Jing Zou1, Menghan Wei1, Qixian Song1
1School of Physics and Electronic Engineering, Sichuan Normal University, Chengdu , 610101, Sichuan, China.
Environmental science and pollution research international
|November 18, 2023
概括
准确的光伏电力预测对于电网稳定性至关重要. 一个新的CEEMDAN-IARO-CBiLSTM模型显著提高了各种条件的预测准确性,超过了现有的方法.
科学领域:
- 可再生能源系统可再生能源系统
- 电网中的人工智能
- 时间序列预测时间序列预测
背景情况:
- 光伏发电 (PV) 越来越多地被整合到电网中,但由于诸如太阳辐射波动等因素,其输出受到间歇性和挥发性的影响.
- 准确预测光伏输出功率对于保持电网稳定性和可靠的能源管理至关重要.
- 现有的预测模型经常与光伏发电的复杂,非线性动态作斗争.
研究的目的:
- 开发一种新的混合模型,以提高光伏发电输出预测的准确性.
- 为应对光伏发电的间歇性和波动性所带来的挑战.
- 提高可再生能源电网集成的可靠性.
主要方法:
- 一个混合模型,CEEMDAN-IARO-CBiLSTM,被提出,整合了完整的集体实证模式分解与适应噪声 (CEEMDAN),改进的人工子优化 (IARO) 和卷积双向长期短期记忆 (CBiLSTM).
- 该模型的输入特征使用皮尔森相关系数方法进行了优化,以确定影响光伏输出功率的有影响因素.
- 该模型的预测准确性在不同的天气条件和季节性变化下与其他既定方法进行了严格的评估.
主要成果:
- 在不同的天气条件下,CEEMDAN-IARO-CBiLSTM模型在预测准确度方面取得了显著的改进,平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 在不同天气条件下减少了至少0.329和0.411.
- 在不同季节观察到MAE和RMSE的进一步减少,这表明了强的表现.
- 统计测试证实,CEEMDAN-IARO-CBiLSTM模型的预测性能优于其他基准方法.
结论:
- 拟议的CEEMDAN-IARO-CBiLSTM混合模型为光伏输出功率预测提供了一个高度准确和可靠的解决方案.
- 输入优化策略增强了模型捕捉关键影响因素的能力.
- 这一进步有助于提高电网稳定性和更有效地整合可再生能源.
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