  This research is concerned with the novel application and investigation of
`Soft Actor Critic' (SAC) based Deep Reinforcement Learning (DRL) to control
the cooling setpoint (and hence cooling loads) of a large commercial building
to harness energy flexibility. The research is motivated by the challenge
associated with the development and application of conventional model-based
control approaches at scale to the wider building stock. SAC is a model-free
DRL technique that is able to handle continuous action spaces and which has
seen limited application to real-life or high-fidelity simulation
implementations in the context of automated and intelligent control of building
energy systems. Such control techniques are seen as one possible solution to
supporting the operation of a smart, sustainable and future electrical grid.
This research tests the suitability of the SAC DRL technique through training
and deployment of the agent on an EnergyPlus based environment of the office
building. The SAC DRL was found to learn an optimal control policy that was
able to minimise energy costs by 9.7% compared to the default rule-based
control (RBC) scheme and was able to improve or maintain thermal comfort limits
over a test period of one week. The algorithm was shown to be robust to the
different hyperparameters and this optimal control policy was learnt through
the use of a minimal state space consisting of readily available variables. The
robustness of the algorithm was tested through investigation of the speed of
learning and ability to deploy to different seasons and climates. It was found
that the SAC DRL requires minimal training sample points and outperforms the
RBC after three months of operation and also without disruption to thermal
comfort during this period. The agent is transferable to other climates and
seasons although further retraining or hyperparameter tuning is recommended.
