Scholarlink

Appliance load monitoring measures the electricity consumption of household appliances, driven by motivations such as financial savings, reducing environmental impact, and predictive maintenance. It provides users with detailed energy usage information, leading to informed decisions about appliance operation and replacement, significant savings, and reduced carbon footprints. Traditionally relying on individual smart meters, a costly and complex method, load monitoring has evolved with non-intrusive load monitoring (NILM), which estimates individual appliance power use by analyzing overall household consumption. This shift, coupled with the exponential data growth from widespread smart meter adoption, necessitates scalable processing techniques. Apache Spark, a distributed processing framework, offers a solution with its scalability and flexibility, handling expanding workloads effectively. However, due to Spark’s lack of built-in support for deep learning and specialized NILM algorithms, in this paper, we explore whether simpler solutions like Random Forests and Gradient-Boosted trees within Spark’s MLlib are able to produce satisfactory results. Our experimental evaluation demonstrated that these algorithms perform well, especially for devices with smoother consumption patterns, highlighting the potential of shallow machine learning techniques in NILM applications, making them practical for real metering systems where data volume and speed rule out more complex solutions.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Leveraging Apache Spark for Appliance-Level Load Monitoring with Ensemble Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Iosif Arvanitis,
  • Menelaos Panagiotis Papastergiou,
  • Agorakis Bompotas,
  • Evangelos Geraridis,
  • Ioanna Giannoukou,
  • Ioannis Karydis,
  • Spyros Sioutas

摘要

Appliance load monitoring measures the electricity consumption of household appliances, driven by motivations such as financial savings, reducing environmental impact, and predictive maintenance. It provides users with detailed energy usage information, leading to informed decisions about appliance operation and replacement, significant savings, and reduced carbon footprints. Traditionally relying on individual smart meters, a costly and complex method, load monitoring has evolved with non-intrusive load monitoring (NILM), which estimates individual appliance power use by analyzing overall household consumption. This shift, coupled with the exponential data growth from widespread smart meter adoption, necessitates scalable processing techniques. Apache Spark, a distributed processing framework, offers a solution with its scalability and flexibility, handling expanding workloads effectively. However, due to Spark’s lack of built-in support for deep learning and specialized NILM algorithms, in this paper, we explore whether simpler solutions like Random Forests and Gradient-Boosted trees within Spark’s MLlib are able to produce satisfactory results. Our experimental evaluation demonstrated that these algorithms perform well, especially for devices with smoother consumption patterns, highlighting the potential of shallow machine learning techniques in NILM applications, making them practical for real metering systems where data volume and speed rule out more complex solutions.