促进工业应用中智能能源管理的非侵入式负载监控:一种主动机器学习方法

Q2 Energy
Lukas Fabri, Daniel Leuthe, Lars-Manuel Schneider, Simon Wenninger
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引用次数: 0

摘要

非侵入式负载监控(NILM)是一种很有前途且经济高效的方法,它结合了从总体消耗推断单个应用的能源消耗的技术,提供了对能源消耗数据的洞察力和透明度。NILM的最大潜力在于工业应用,在不过度计量的情况下促进能源监测和异常检测等关键优势。然而,除了缺乏可行的工业时间序列数据外,NILM在工业应用中的主要挑战是标记数据的稀缺性,导致昂贵且耗时的工作流程。为了克服这个问题,我们开发了一个使用真实世界数据的主动学习模型,以智能地选择最具信息量的数据进行专家标记。我们通过三种查询策略有效地选择训练数据子集来识别需要标记的数据,从而将三种分解算法与基准模型进行比较。我们表明,主动学习模型在最小的用户输入下获得了令人满意的精度。我们的结果表明,与100%标记训练数据的基准相比,我们的模型减少了用户输入,即标记数据,最多减少了99%,同时实现了62%到80%的预测精度。主动学习模型有望通过解决关键的市场障碍,特别是通过最小化工人密集型数据标签来降低实施成本,从而成为扩大NILM在工业应用中的采用的基础。在这方面,我们的工作为进一步优化主动学习模型的体系结构奠定了基础,或者作为工业应用中NILM主动学习的第一个基准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fostering non-intrusive load monitoring for smart energy management in industrial applications: an active machine learning approach

Non-intrusive load monitoring (NILM) is a promising and cost-effective approach incorporating techniques that infer individual applications' energy consumption from aggregated consumption providing insights and transparency on energy consumption data. The largest potential of NILM lies in industrial applications facilitating key benefits like energy monitoring and anomaly detection without excessive submetering. However, besides the lack of feasible industrial time series data, the key challenge of NILM in industrial applications is the scarcity of labeled data, leading to costly and time-consuming workflows. To overcome this issue, we develop an active learning model using real-world data to intelligently select the most informative data for expert labeling. We compare three disaggregation algorithms with a benchmark model by efficiently selecting a subset of training data through three query strategies that identify the data requiring labeling. We show that the active learning model achieves satisfactory accuracy with minimal user input. Our results indicate that our model reduces the user input, i.e., the labeled data, by up to 99% while achieving between 62 and 80% of the prediction accuracy compared to the benchmark with 100% labeled training data. The active learning model is expected to serve as a foundation for expanding NILM adoption in industrial applications by addressing key market barriers, notably reducing implementation costs through minimized worker-intensive data labeling. In this vein, our work lays the foundation for further optimizations regarding the architecture of an active learning model or serves as the first benchmark for active learning in NILM for industrial applications.

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来源期刊
Energy Informatics
Energy Informatics Computer Science-Computer Networks and Communications
CiteScore
5.50
自引率
0.00%
发文量
34
审稿时长
5 weeks
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