利用多模式数据来预测强制性传输中断的严重程度,并使用层次的时空多重网络
Rafaa Aljurbua1,2, Jumanah Alshehri1,3, Shelly Gupta1
1Center for Data Analytics and Biomedical Informatics, Computer and Information Science Department, Temple University, Philadelphia, Pennsylvania, United States of America.
PloS one
|June 25, 2025
概括
准确的停电预测对于减轻天气事件影响至关重要. 一个新的分层多路网络 (HMN-RTS) 改善了3小时前的停电持续时间预测,提高了电网的弹性.
科学领域:
- 电气工程 电气工程
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 长期停电严重影响经济和生活质量.
- 对这些干扰的准确和早期警告是一个关键的挑战.
- 现有的预测方法往往缺乏多模式数据集成.
研究的目的:
- 引入和评估分层多重组网络可靠性传输系统 (HMN-RTS) 模型.
- 预测强制性传输中断的持续时间,使用多模式方法.
- 加强对电网中断的早期预警系统.
主要方法:
- 开发了一个分层多重网络 (HMN-RTS),集成多种数据源.
- 收集和分析了太平洋西北地区两年的县级数据.
- 利用了停电数据,天气数据/预报,闪电,土地覆盖,输电线路和社交媒体.
主要成果:
- HMN-RTS模型在预测停电持续时间方面表现出更高的准确性.
- 在3小时前的预测中获得了0.79的宏观F1得分,超过了替代方案 (0.73).
- 展示了跨越多个时间和季节的可靠预测.
结论:
- 该HMN-RTS模型为预测停电持续时间提供了更高的准确性.
- 使电网运营商能够通过早期风险评估及时实施缓解策略.
- 强调多模式数据集成对电力系统可靠性的价值.
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