Distribution within a private building

In terms of energy generation, PV panels are selected, they generate sustainable and clean energy for two electric heaters (TES heater and Air recovery Unit). An inverter is installed between PV panels and heaters which serves to invert DC to AC. TES (thermal energy storage) is used to store thermal energy and the PCM (Phase changing materials) are used to fill the inside of the TES. The heat pump is chosen to be the heating operation equipment to circulate hot and cold water. An air recovery unit is installed to offer ventilation and heat up/cool down the indoor air. Smart sensors are deployed in these systems, data will be generated and sent to a central monitor system which allows energy usage to be more sufficient. The system can make smart decisions regarding energy demand, weather condition and user behaviours. 

The distribution of HVAC systems for houses can also be applicable and scalable in a shared or public building.  In such buildings, an advanced monitoring platform can be added to increase building performance in terms of the energy management system and the HVAC system. 

The monitoring system should provide functionalities that allow users and building manager to track and review energy performance, HVAC performance and financial performance. Additionally, the monitoring platform also featured with self-diagnosis function, fault detection and accurate maintenance, when an abnormal occurs, the monitor platform should rapidly responses to building owners and technical team to minimize the consequence. 

Private buildings and public buildings are connected via a smart grid, this could balance the energy demand and supply in a neighbourhood. 

In a neighborhood level, the focus should be given more on the data analytic perspective. Based on the literature review, collaborative data analytical can be used to monitor the energy management system in a neighborhood. In the Brainport smart district, buildings with similar characteristics in terms of energy performance can be given a cluster, by categorizing different buildings in a neighborhood allows us to obtain a clustering system, where data analytical can be applied in this system, this enables the estimation of energy consumption both at individual and group level and then aggregating them to predict the overall energy generation and demand.