Building plug load mode detection, forecasting and scheduling

Lola Botman, Jesus Lago, Xiaohan Fu, Keaton Chia, Jesse Wolf, Jan Kleissl and Bart De Moor

Applied Energy, 2024, vol. 364, issue C, No S0306261924004811

Abstract: In an era of increasing energy demands and environmental concerns, optimizing energy consumption within buildings is crucial. Despite the vast improvements in HVAC and lighting systems, plug loads remain an under-studied area for enhancing building energy efficiency. This paper studies smart plug active operating mode detection, plug-level load forecasting, and plug scheduling methodologies. This research leverages a unique dataset from the University of California, San Diego, consisting of readings from over 150 smart plugs in several office buildings for more than a year, notably during the post-Covid era. This dataset is made publicly available. A comprehensive literature review on plug, i.e., appliances operating mode detection is presented. Novel unsupervised learning approaches are applied to identify plug operating modes. A pipeline integrating the detected modes with forecasting and scheduling is developed, aiming at building energy consumption reduction. Our findings offer valuable insights and promising results into smart plug management for energy-efficient buildings.

Keywords: Smart plug; Operating mode; Plug scheduling; Building consumption (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123098

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