在北克居住区的负载监控可行的解决方案
Sayed Saeed Hosseini1, Benoit Delcroix2, Nilson Henao1
1Department of Electrical and Computer Engineering, Laboratoire d'innovation et de Recherche en Énergie Intelligent, Hydrogen Research Institute, Université du Québec à Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
非侵入性负载监控 (NILM) 在北克住宅中面临挑战,原因是特定地区的电力使用. 本研究提出了一种混合方法,将侵入性和非侵入性方法结合起来,以提高NILM的可行性.
科学领域:
- 能源系统工程 能源系统工程
- 人工智能在能源中的作用
背景情况:
- 非侵入性负载监控 (NILM) 旨在进行分类能源监控,但在住宅环境中面临实际限制.
- 现有的NILM研究严重依赖于欧洲和美国的数据集,忽视了不同的地理和行为背景.
研究的目的:
- 调查在北克居民中实施NILM的障碍.
- 为北克特定的住宅环境量身定制的负载识别和分类提出一种新的混合方法.
主要方法:
- 北克住宅能源数据的比较统计分析.
- 开发一种结合式负载识别方法,将子计量与NILM集成在一起.
- 监督和无监督机器学习的应用,用于加热需求和剩余负载分类.
主要成果:
- 北克的特定地理位置的电力消耗模式对现有的NILM方法的性能产生了重大影响.
- 拟议的混合方法显示了在研究区域缓解NILM困难的潜力.
结论:
- 尼尔姆系统的可行性严重依赖于解决地理特定的消费特征.
- 进一步的研究和量身定制的方法对于在北克等多样化的住宅环境中成功实施NILM至关重要.
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