基于邻域成分分析和正则化极端学习机的家庭能源管理系统负载识别

T. W. Cabral, F. B. Neto, E. R. De Lima, Gustavo Fraidenraich, L. G. P. Meloni
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引用次数: 0

摘要

住宅环境中的高效能源管理是一项长期挑战,其中家庭能源管理系统(HEMS)在优化能源消耗方面发挥着至关重要的作用。负载识别可以识别活动电器,为 HEMS 提供稳健性。家用电器的精确识别是一个尚未完全探索的领域。通过专用于类别之间可分性的技术和可增强可靠性的模型来提高分类性能等方面的差距仍然存在。这项工作改进了 HEMS 应用中负载识别的几个方面。在这项研究中,我们采用邻域成分分析法(NCA)从数据中提取相关特征,寻求类之间的可分离性。我们还采用了正则化极限学习机(RELM)来识别家用电器。这种开创性的方法提高了性能,其准确率和加权 F1-Score 值(分别为 97.24% 和 97.14%)超过了最先进的方法,而且根据 Kappa 指数,可靠性更高(即 0.9388),优于竞争分类器。这些证据凸显了机器学习(ML)技术(特别是 NCA 和 RELM)在住宅环境负荷识别和能源管理方面的巨大潜力。
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Load Recognition in Home Energy Management Systems Based on Neighborhood Components Analysis and Regularized Extreme Learning Machine
Efficient energy management in residential environments is a constant challenge, in which Home Energy Management Systems (HEMS) play an essential role in optimizing consumption. Load recognition allows the identification of active appliances, providing robustness to the HEMS. The precise identification of household appliances is an area not completely explored. Gaps like improving classification performance through techniques dedicated to separability between classes and models that achieve enhanced reliability remain open. This work improves several aspects of load recognition in HEMS applications. In this research, we adopt Neighborhood Component Analysis (NCA) to extract relevant characteristics from the data, seeking the separability between classes. We also employ the Regularized Extreme Learning Machine (RELM) to identify household appliances. This pioneering approach achieves performance improvements, presenting higher accuracy and weighted F1-Score values—97.24% and 97.14%, respectively—surpassing state-of-the-art methods and enhanced reliability according to the Kappa index, i.e., 0.9388, outperforming competing classifiers. Such evidence highlights the promising potential of Machine Learning (ML) techniques, specifically NCA and RELM, to contribute to load recognition and energy management in residential environments.
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