Abstract
In the past few decades, extensive research has been conducted to develop operation and control strategy
for smart buildings with the purpose of reducing energy consumption. Besides studying on single
building, it is envisioned that the next generation buildings can freely connect with one another to share
energy and exchange information in the context of smart grid. It was demonstrated that a network of
connected buildings (aka building clusters) can significantly reduce primary energy consumption,
improve environmental sustainability and building’s resilience capability. However, an analytic tool to
determine which type of buildings should form a cluster and what is the impact of building clusters’
heterogeneity based on energy profile to the energy performance of building clusters is missing. To bridge
these research gaps, we propose a self-organizing map clustering algorithm to divide multiple buildings
to different clusters based on their energy profiles, and a homogeneity index to evaluate the heterogeneity
of different building clusters configurations. In addition, a bi-level distributed decision model is
developed to study the energy sharing in the building clusters. To demonstrate the effectiveness of the
proposed clustering algorithm and decision model, we employ a dataset including monthly energy
consumption data for 30 buildings where the data is collected every 15 min. It is demonstrated that
the proposed decision model can achieve at least 13% cost savings for building clusters. The results show
that the heterogeneity of energy profile is an important factor to select battery and renewable energy
source for building clusters, and the shared battery and renewable energy are preferred for more
heterogeneous building clusters.
