An IoT Solution for Energy Management at Building and District Level

Today the monitoring of energy variables plays a key-role in the proper management of buildings to optimize costs along with the low emission profile and comfortable environments. The monitoring could be augmented by adding also optimization aspects that could decrease the energy/costs. Up to now the optimization is performed by using building and context (district) data, but with no or rough evaluation of comfort conditions delivered to the occupants. The work presented in this paper is derived from the ENERGIS project that takes into account individual buildings and districts including detailed comfort conditions representing a novelty in the Energy Management System. The monitoring is used to locally sense the energy demand while the optimization is performed at two different scales. The first optimization tries to consider different aspects related to the thermal management of rooms, supported by a novel sensor that is able to evaluate the comfort taking into account the room model and to control the thermal actuators to track a comfort set-point. The second level of optimization starts from the collected data from each building to set-up a district model that is able to map and then predict the energy demand enabling an energy management that is built on the concept of “sharing”. This paper outlines the overall system architecture that exploits the benefit of IoT also showing the first optimization level performed on a business office showing the overall pipeline that starts from the sensing of environment and ends with the control of actuators to track an optimized set-point