User-Centered Nonintrusive Electricity Load Monitoring for Residential Buildings

TLDR

The study introduces a user‑centered, nonintrusive load‑monitoring system that delivers appliance‑level energy consumption and usage schedule feedback in residential homes. It employs lightweight voltage‑current event‑detection and classification algorithms run at the main circuit panel, with user‑guided calibration and interaction to refine performance, evaluated on laboratory, house, and apartment data. The detector achieved a 94 % true‑positive rate with a 0.26 % false‑positive rate, and overall event detection and classification accuracy reached 82 %.

Abstract

This paper presents a nonintrusive electricity load-monitoring approach that provides feedback on the energy consumption and operational schedule of electrical appliances in a residential building. This approach utilizes simple algorithms for detecting and classifying electrical events on the basis of voltage and current measurements obtained at the main circuit panel of the home. To address the necessary training and calibration, this approach is designed around the end-user and relies on user input to continuously improve its performance. The algorithms and the user interaction processes are described in detail. Three data sets were collected with a prototype system (from a power strip in a laboratory, a house, and an apartment unit) to test the performance of the algorithms. The event detector achieved true positive and false positive rates of 94 and 0.26%, respectively. When combined with the classification task, the overall accuracy (correctly detected and classified events) was 82%. The advantages and limitations of this work are discussed, and possible future research is presented.