在城市照明系统中使用自动编码器和变压器算法检测异常
Tomasz Śmiałkowski1, Andrzej Czyżewski2
1TSTRONIC Sp. z o. o., 19 Benzynowa St., 83-011, Gdańsk, Poland. tomasz.smialkowski@tstronic.eu.
Scientific reports
|October 15, 2025
概括
机器学习算法有效地检测道路照明系统中的异常. 自动编码器模型在实时能源消耗分析和系统管理方面表现出卓越的准确性和效率.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 道路照明管理系统需要持续的实时异常检测.
- 在线异常检测对于保持系统可靠性和效率至关重要.
研究的目的:
- 评估机器学习算法在照明系统中异常检测的有效性.
- 将Autoencoder和变压器算法的性能与传统方法进行比较.
主要方法:
- 使用Autoencoder和变压器机器学习算法分析电表记录.
- 与简单的能源消耗比较机制进行比较.
- 使用分类指标进行评估:错误矩阵,灵敏度,精度和F1分数.
主要成果:
- 自动编码器在较低的计算成本下实现了0.9565的高F1得分.
- 变压器证明了有效的异常检测,F1得分为0.8125.5.
- 在ILED平台上的自动编码器实现在15分钟的延迟内提供异常检测.
结论:
- 机器学习算法为照明系统中的异常检测提供了显著的优势.
- 自动编码器是城市照明管理的高度准确和资源高效的解决方案.
- 该研究强调了通过人工智能驱动的异常检测来提高照明系统可靠性和效率的潜力.
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