  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.
