一个基于集群算法的机器学习整体框架,用于提高电能消耗性能
Taeyong Sim1, Sanghyun Ryu1, Dongjun Lee1
1Department of Artificial Intelligence, Sejong University, Seoul, 05006, Republic of Korea.
Scientific reports
|November 17, 2025
概括
这项研究通过一种新的整体方法来增强电能预测. 通过集群建筑物和应用机器学习模型,它可以实现更准确的能源消耗预测,以提高效率.
科学领域:
- 能源科学 能源科学
- 数据科学数据科学数据科学
- 建筑科学 建筑科学
背景情况:
- 准确的电能预测对于电网管理和用户满意度至关重要.
- 识别建筑物中不同的消费模式是提高预测准确性的关键.
- 现有的机器学习 (ML) 模型往往缺乏针对不同能源使用配置的特异性.
研究的目的:
- 开发和评估一套集体ML方法,用于精确预测电能消耗.
- 将集群算法与ML模型集成,以识别和利用建筑特定的消费模式.
- 通过改善住宅建筑中的预测来加强能源管理策略.
主要方法:
- 应用集群算法 (K-Means变体) 来根据能源使用量对住宅建筑进行分类.
- 评估了五个ML模型 (CatBoost,决策树,LightGBM,随机森林,XGBoost) 以确定集群中的预测性能.
- 通过将每个集群的高性能ML算法结合起来,开发集体模型来预测总能耗.
主要成果:
- 根据每月的能源数据,最佳集群确定了两个不同的房子组.
- CatBoost和LightGBM表现出卓越的个人预测性能.
- 所有开发的组合模型在没有集群的情况下显著优于传统的ML方法 (p < 0.05或0.01).
结论:
- 拟议的基于集群的ML组合模型通过考虑独特的使用模式,准确地预测建筑物的能源消耗.
- 这种方法在能源预测中比非集群的ML方法提供了显著的改进.
- 预计这些发现将有助于制定有效的减少能源消耗的战略.
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