基于mapreduce的电力消费行为和天气因素之间的关联分析研究
1School of Computer Science, Northeast Electric Power University, Jilin, 132012, China. yyh0504@126.com.
Scientific reports
|May 7, 2025
概括
本研究介绍了使用MapReduce进行并行关联规则挖掘方法,以分析受天气影响的电力消耗模式. 该方法有效地发现大数据集中的关系,改善数据挖掘能力.
科学领域:
- 数据科学数据科学数据科学
- 计算机科学 计算机科学
- 能源系统 能源系统
背景情况:
- 电力消费行为受到气候变化因素的重大影响.
- 高效地挖掘大规模的电力消耗数据对于理解这些行为变化至关重要.
- 现有的方法可能无法充分处理能源消耗数据的数量和复杂性.
研究的目的:
- 开发一种并行关联规则挖掘方法,用于发现电力消耗行为与天气因素之间的关联.
- 解决大规模电力消耗数据集的高效数据挖掘需求.
- 为了提高协会规则挖掘能源数据的可扩展性和可用性.
主要方法:
- 使用基于密度的K-平均值方法并行计算样本密度,对电力消耗数据进行分离.
- 集群电力数据与相应的天气数据的整合.
- 基于阿普里奥里算法实现并行频繁项目集采矿和协会规则采矿.
- 优化MapReduce工作使用Combiner函数与本地支持值来减少数据传输.
主要成果:
- 拟议的并行关联规则采矿方法有效地识别了天气因素和电力消耗之间的关系.
- 在Hadoop集群上的实验结果证明了该方法的可用性和可扩展性.
- 在MapReduce框架中,使用Combiner函数显著降低了数据传输开销.
结论:
- 开发的并行关联规则挖掘方法为分析应对天气变化的电力消耗行为提供了高效和可扩展的解决方案.
- 数据离散,集群和并行挖掘技术的整合为能源部门的大数据分析提供了强大的方法.
- 这项研究有助于更好地了解能源消耗动态,并支持更有效的能源管理策略.
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