  Advances in renewable energy generation and introduction of the government
targets to improve energy efficiency gave rise to a concept of a Zero Energy
Building (ZEB). A ZEB is a building whose net energy usage over a year is zero,
i.e., its energy use is not larger than its overall renewables generation. A
collection of ZEBs forms a Zero Energy Community (ZEC). This paper addresses
the problem of energy sharing in such a community. This is different from
previously addressed energy sharing between buildings as our focus is on the
improvement of community energy status, while traditionally research focused on
reducing losses due to transmission and storage, or achieving economic gains.
We model this problem in a multi-agent environment and propose a Deep
Reinforcement Learning (DRL) based solution. Each building is represented by an
intelligent agent that learns over time the appropriate behaviour to share
energy. We have evaluated the proposed solution in a multi-agent simulation
built using osBrain. Results indicate that with time agents learn to
collaborate and learn a policy comparable to the optimal policy, which in turn
improves the ZEC's energy status. Buildings with no renewables preferred to
request energy from their neighbours rather than from the supply grid.
