A Micro-Moment System for Domestic Energy Efficiency Analysis

Domestic user behavior is a crucial factor guiding overall power consumption, necessitating the development of systems that analyze and help shape energy-efficient behavior. Therefore, the most important step in the process is the collection and understanding of highly detailed domestic consumption data. This article presents an appliance-based energy data collection and analysis system for energy efficiency applications. It leverages the concept of micro-moments, which are short-timed and energy-based events that form the overall energy behavior of the end user. The system comprises sensing modules for recording energy consumption, occupancy, temperature, humidity, and luminosity storing recordings on a database server. Sensing parameters were tested in terms of connection stability and measurement accuracy. A four-week contextual appliance-level dataset has been collected from research cubicles. Collected data were also classified into corresponding micro-moments with a variety of classifiers including ensemble decision trees and deep learning, achieving high stability and accuracy of 99%. Further, the micro-moment usage efficiency is calculated to quantify the efficiency of usage at the appliance level.

Automated summary

This article presents an appliance-based energy data collection and analysis system that utilizes the concept of micro-moments to analyze the overall energy behavior of end users. By testing sensing parameters for connection stability and measurement accuracy, the system collects detailed data including energy consumption, occupancy, temperature, humidity, and luminosity, classifying them into micro-moments for high stability and accuracy.