基于分支错误减少标准的信号递归分解及其应用于风能发电预测的应用
PloS one
|March 22, 2024
概括
本研究引入了变化模式分解 (VMD) 的新方法,以提高时间序列预测的准确性. 适应性方法优化了模式号,提高了对风力发电等任务的预测.
科学领域:
- 时间序列分析分析时间序列分析
- 信号处理 信号处理
- 预测可再生能源的发展
背景情况:
- 变化模式分解 (VMD) 是混合时间序列预测模型中常用的工具.
- 它的准确性受限于手动选择模式号码,导致过低或过度分解.
- 优化VMD的模式号码对于提高预测性能至关重要.
研究的目的:
- 开发一种适应方法来确定VMD中的模式号.
- 为了提高时间序列预测混合模型的准确性.
- 将改进的VMD方法应用于风能发电预测.
主要方法:
- 开发了一个新的分支错误减少 (BER) 标准.
- 基于VMD的适应性递归分解方法使用BER标准实施.
- 适应性VMD方法与标准预测模型集成.
主要成果:
- 拟议的自适应VMD方法有效地确定了最佳模式号码.
- 将适应性VMD与预测模型集成,显著提高了准确性.
- 实验结果证实了该方法在风力发电预测中的有效性.
结论:
- 适应性VMD方法克服了手动模式编号选择的局限性.
- 这种方法为时间序列预测提供了更强大,更准确的解决方案.
- 该研究验证了拟议的技术在可再生能源预测中的实际应用.
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