Linearised data-driven LSTM-based control of multi-input HVAC systems

The pursuit of sustainability has paved the way for building management systems (BMSs) that can steer buildings in an energy-efficient way. In this article, a deep learning approach is proposed to control multi-input HVAC systems in order to minimize both thermal discomfort and operational cost. More particularly, an LSTM-based encoder-decoder process model, trained on historical weather data and control sequences generated while the building was steered by a modern rule-based controller (RBC), is fed into an optimisation problem, to which a change of variables is applied to efficiently model the effect of interdependent control inputs. Both the nonlinear LSTM process model and the cost function of the optimisation problem are linearised to formulate the control problem as a mixed integer linear programming (MILP) problem, which ensures that the controller can operate in near real-time and with limited computational power. Moreover, to avoid resorting to model extrapolation and to improve the model’s predictive performance, the set of allowed control signal values is restricted using a quantile-based approach. In addition to the purely data-driven controller (DDC), a hybrid controller is designed to leverage the strengths of the RBC and the DDC. The performance of both controllers is benchmarked against the RBC’s performance using the BOPTEST simulation environment under various experiment settings, highlighting how the hyperparameters affect the controller’s performance. Compared to the RBC, we show that the proposed controllers realise substantial improvements in terms of both thermal comfort and operational cost while controlling a single zone or two zones simultaneously.