一个强大的最大电流预测模型,用于具有异常值的时间序列
1College of Computer, Xi'an Aeronautical Institute, Xi'an, Shaanxi Province, China.
PeerJ. Computer science
|June 22, 2023
概括
本研究引入了一个强大的最大电流自行回归 (MCAR) 模型用于时间序列预测. 与传统方法相比,MCAR模型有效地减少了异常干扰,提高了预测准确度.
科学领域:
- 时间序列分析时间序列分析.
- 强大的统计数据.
- 信号处理 信号处理
背景情况:
- 传统的时间序列预测模型与异常损坏的数据作斗争,降低了可靠性和准确性.
- 异常值在实际应用中显著影响自回归模型的性能.
- 准确的电力需求预测对于电网管理和稳定性至关重要.
研究的目的:
- 提出一个强大的最大电流率自行回归 (MCAR) 预测模型.
- 在异常值存在的情况下,提高时间序列预测的准确性.
- 通过使用真实功率序列数据对比深度学习方法来评估MCAR模型的性能.
主要方法:
- 开发了一个强大的MCAR模型,结合了最大电流原则.
- 利用高斯核宽度对相关性来测量本地数据相似性.
- 在MCAR模型中用于参数估计的半确定的放松.
- 将模型应用于来自中国汉中市的实际功率序列数据.
主要成果:
- 与深度学习方法相比,MCAR模型显示出更高的性能.
- MCAR实现了1.63%低于平均值的平均绝对百分比误差 (MAPE).
- 证实最大电流是有效的减轻异常干扰.
结论:
- 拟议的MCAR模型为具有异常值的时间序列预测提供了强大而准确的解决方案.
- 最大电流是提高自回归模型可靠性的宝贵工具.
- 该MCAR模型显示了在电力系统预测中应用的巨大潜力.
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