使用数据流预测停电情况:采用自适应集体学习方法,采用基于功能和性能的权重机制
Elnaz Kabir1, Seth D Guikema2, Steven M Quiring3
1Department of Engineering Technology & Industrial Distribution, Texas A&M University, College Station, Texas, USA.
Risk analysis : an official publication of the Society for Risk Analysis
|September 4, 2023
概括
这项研究引入了一种自适应集体学习算法,用于预测由天气事件引起的停电. 新模型平均提高了8%的预测准确度,有助于更快地恢复电力.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 电气工程 电气工程
背景情况:
- 风暴和热浪等天气事件严重破坏电力系统,导致停电和不便.
- 准确预测客户停电对于有效的公用事业恢复工作至关重要.
- 目前的模型与数据流以及来自各种天气事件数据的模型不确定性作斗争.
研究的目的:
- 开发一种适应性,全天候停电预测模型,能够处理数据流.
- 解决现有模型在管理各种天气数据和相关不确定性方面的局限性.
主要方法:
- 提出了一个针对数据流设计的自适应集体学习算法.
- 实施了基于特征和绩效的权重机制,以结合基础学习者的输出.
- 为了模型开发和验证,利用了大量的日常客户中断的真实数据集.
主要成果:
- 与基准方法相比,拟议的算法证明了更准确的概率预测.
- 实现了从4%到22%的概率预测误差的减少,平均降低了8%.
- 当使用更简单的模型作为基础学习者时,观察到增强的性能改善.
结论:
- 开发的自适应集体学习算法有效地预测数据流的停电情况.
- 该模型在概率和断点预测方面提供了显著的改进,有助于公用事业的响应.
- 这种方法代表了在电力系统中预测全天候中断的新解决方案.
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