智能电表数据分析在推动可持续发展中的作用
Archana Y Chaudhari1, Preeti Mulay2, Shradha Chavan3
1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.
MethodsX
|February 25, 2025
概括
智能电表 (SEM) 数据分析对于可持续性至关重要. 一个新的基于密度的高斯混合增量集群 (CGMIC) 算法有效地更新电力负载模式,避免了昂贵的大数据重新集群.
科学领域:
- 数据科学数据科学数据科学
- 能源系统分析 能源系统分析
- 机器学习 机器学习
背景情况:
- 智能电力计 (SEM) 数据对于电力系统的可持续性至关重要,但其大量数据对传统集群方法构成挑战.
- 增量学习对于有效处理不断增长的SEM数据集至关重要.
- 现有的方法需要在新数据到来时从头开始重新聚类所有数据,这在计算上是昂贵的.
研究的目的:
- 为智能电力计 (SEM) 数据分析开发一个高效的增量集群算法.
- 为了应对更新电力负载模式的挑战,而不需要重新聚合整个数据集.
- 通过更好地了解电力消费行为,为可持续发展目标做出贡献.
主要方法:
- 提出了基于近距离的高斯混合增量集群 (CGMIC) 算法.
- CGMIC从新数据中提取负载模式,并逐步集成或形成新的模式.
- 使用IITB印度住宅能源数据集验证了算法,并使用轮得分和戴维斯博尔丁指数将性能与DBSCAN进行比较.
主要成果:
- CGMIC算法有效地逐步更新负载模式,避免需要完全重新聚类数据.
- 使用轮得分和戴维斯·博尔丁指数的绩效评估证明了CGMIC的有效性.
- 该算法成功识别了住宅电力消费行为的变化.
结论:
- 通过逐步更新负载模式,CGMIC提供了一种有效的解决方案来分析大规模的SEM数据.
- 来自CGMIC的见解有助于理解和预测住宅电力消费的变化.
- 这项研究支持公用事业公司优化电力负载管理,为可持续能源实践做出贡献.
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