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

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