Energy Disaggregation of Industrial Machinery Utilizing Artificial Neural Networks for Non-intrusive Load Monitoring

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Philipp Pelger , Johannes Steinleitner , Alexander Sauer
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引用次数: 0

Abstract

This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.

Abstract Image

利用人工神经网络对工业机械进行能量分解,实现非侵入式负载监测
本文探讨了非侵入式负荷监测技术在工业领域的应用,以分解生产过程中的机械能耗。随着人们越来越重视能源效率和去碳化措施,实现生产过程中的能源透明度变得至关重要。利用非侵入式负荷监测、能源数据分析和处理,可以为提高能效和减少排放的知情决策提供有价值的见解。虽然非侵入式负荷监测在建筑和住宅领域得到了广泛研究,但在工业制造领域的应用还有待进一步探索。本文针对这一研究空白,将成熟的非侵入式负荷监测技术应用于工业数据集。通过采用人工神经网络进行能量分解,可以确定工业机械的能耗。因此,利用设计科学研究方法开发了一种普遍适用的跨能源载体方法,用于分解制造过程中的机械能耗,并通过利用压缩空气演示器进行的实际案例研究进行了验证。研究结果表明,人工神经网络非常适合用于工业数据的能耗分解,能有效识别开和关状态、多级状态和连续可变状态。在研究能耗评估中的新兴人工智能技术时,应进一步考虑非侵入式负荷监测。它可以成为侵入式负载监控的可行替代方案,也是为每台机器安装能源计量表的先决条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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