Abstract
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.
Published Version
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