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

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