基于改进的希尔伯特-黄变换方法加上NAR动态神经网络模型的短期风速预测
Jian Chen1, Zhikai Guo1, Luyao Zhang1
1Water Conservancy College, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
Scientific reports
|January 5, 2024
概括
准确的风速预测对于稳定的风能发电至关重要. 这项研究引入了改进的希尔伯特-黄变换 (HHT) 与互补集体实证模式分解 (CEEMD) 和动态神经网络,用于增强风速预测.
科学领域:
- 可再生能源系统可再生能源系统
- 数据科学和预测建模预测模型
- 电力系统工程 电力系统工程
背景情况:
- 风能是一种快速增长的可再生能源,对于清洁发电至关重要.
- 风速间歇性,随机性和无法控制性对电力系统稳定性和运营成本构成重大挑战.
- 准确的风速预测对于安全的设备运行,电网整合和稳定的电力系统运行至关重要.
研究的目的:
- 提高风力发电风速预测模型的准确性和稳定性.
- 降低与风能发电相关的经济成本,促进可持续能源发展.
- 解决传统的实证模式分解 (EMD) 方法在处理复杂的风速数据方面的局限性.
主要方法:
- 使用改进的希尔伯特 - 黄变换 (HHT) 与互补集体实证模式分解 (CEEMD) 来处理风速数据,减轻组件模式混合和噪声等问题.
- 采用数学分析方法计算组件重量,用于构建动态神经网络模型.
- 优化了HHT-CEEMD方法,使用神经适应回归 (NAR) 模型进行时间序列预测.
主要成果:
- 开发的HHT-CEEMD-NAR模型在风速预测准确度方面取得了显著的改进.
- 该模型有效地处理了风速数据固有的随机和非线性特征.
- 与传统方法相比,在新疆地区的应用显示了较小的平方根平均误差和增强的确定系数.
结论:
- 改进的HHT-CEEMD-NAR模型为预测风速提供了一个强大而准确的预测工具.
- 这种方法提高了风力发电整合到电网中的效率和可靠性.
- 该研究提供了一种有价值的方法来优化风能发电调度并确保稳定的电力系统运行.
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