Reshaping consumption habits by exploiting energy-related micro-moment recommendations: A case study

TL;DR

This paper presents an integrated system that detects consumption patterns and uses micro-moment recommendations to reduce energy use and promote sustainable habits in office environments.

Contribution

It introduces a novel energy reduction system leveraging sensors, smart meters, and micro-moment recommendations to shape user habits and decrease unnecessary energy consumption.

Findings

System successfully reduces energy consumption in office scenarios.

Micro-moment recommendations effectively trigger user actions for energy saving.

Implementation on open source platform demonstrates practical viability.

Abstract

The environmental change and its effects, caused by human influences and natural ecological processes over the last decade, prove that it is now more prudent than ever to transition to more sustainable models of energy consumption behaviors. User energy consumption is inductively derived from the time-to-time standards of living that shape the user's everyday consumption habits. This work builds on the detection of repeated usage consumption patterns from consumption logs. It presents the structure and operation of an energy consumption reduction system, which employs a set of sensors, smart-meters and actuators in an office environment and targets specific user habits. Using our previous research findings on the value of energy-related micro-moment recommendations, the implemented system is an integrated solution that avoids unnecessary energy consumption. With the use of a messaging API, the system recommends to the user the proper energy saving action at the right moment and gradually shapes user's habits. The solution has been implemented on the Home Assistant open source platform, which allows the definition of automations for controlling the office equipment. Experimental evaluation with several scenarios shows that the system manages first to reduce energy consumption, and second, to trigger users' actions that could potentially urge them to more sustainable energy consumption habits.