机器学习模型开发预测停电时间 (POD):电力公用事业的案例研究.
Bita Ghasemkhani1, Recep Alp Kut2, Reyat Yilmaz3
1Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir 35390, Turkey.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
本研究引入了一种机器学习模型,用于预测停电持续时间,改善电力公用事业管理. 这种新的方法实现了98.433%的准确性,大大提高了电网可靠性和客户沟通.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 气候变化和电网复杂性挑战停电管理.
- 对电力公用事业而言,有效预测停电持续时间至关重要.
- 实时客户反对于管理中断影响至关重要.
研究的目的:
- 开发一个新的预测模型,用于电力中断的持续时间.
- 加强电力公用事业中断管理和客户沟通.
- 通过先进的分析来提高电网的弹性和可靠性.
主要方法:
- 利用机器学习算法:决策树 (DT),随机森林 (RF),k-最近邻居 (KNN) 和极端梯度增强 (XGBoost).
- 使用来自土耳其电力公司的传感器和非传感器停电历史数据.
- 应用的最小冗余最大相关性 (MRMR) 用于XGBoost.功能选择.
主要成果:
- 使用MRMR的XGBoost模型在预测中断持续时间方面实现了98.433%的准确性.
- 这比最先进的方法提高了12.922% (平均准确率为85.511%).
- 该模型展示了适应各种电网结构和中断原因的适应性.
结论:
- 机器学习为增强停电管理提供了一个实用的解决方案.
- 开发的模型显著提高了预测准确性和电网可靠性.
- 这种方法改变了停电期间的电力公用事业公司响应和客户沟通.
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