  In building management, usually static thermal setpoints are used to maintain
the inside temperature of a building at a comfortable level irrespective of its
occupancy. This strategy can cause a massive amount of energy wastage and
therewith increase energy related expenses. This paper explores how to optimise
the setpoints used in a particular room during its unoccupied periods using
machine learning approaches. We introduce a deep-learning model based on
Recurrent Neural Networks (RNN) that can predict the temperatures of a future
period directly where a particular room is unoccupied and by using these
predicted temperatures, we define the optimal thermal setpoints to be used
inside the room during the unoccupied period. We show that RNNs are
particularly suitable for this learning task as they enable us to learn across
many relatively short series, which is necessary to focus on particular
operation modes of the air conditioning (AC) system. We evaluate the prediction
accuracy of our RNN model against a set of state-of-the-art models and are able
to outperform those by a large margin. We furthermore analyse the usage of our
RNN model in optimising the energy consumption of an AC system in a real-world
scenario using the temperature data from a university lecture theatre. Based on
the simulations, we show that our RNN model can lead to savings around 20%
compared with the traditional temperature controlling model that does not use
optimisation techniques.

Machine Learning;Neural and Evolutionary Computing;Machine Learning