A feasibility study of machine learning-based model predictive control for commercial buildings in cooling season

This study explores the feasibility of model-based predictive control (MPC) with a machine learning (ML) approach in commercial buildings. Grey-box and ML-based models were developed using experimental data from a test facility. Three different models were considered for ML-based model development: artificial neural network, gauss process regression, and support vector regression. In MPC simulations, the optimal solution for the grey-box model was achieved by applying linear programming, assuming a linear time-invariant model. On the other hand, the proposed ML-based method utilized predefined setpoint trajectories to achieve cost savings by load shifting from on-to off-peak. The estimated trajectory yielding the minimum cost was identified as the optimal trajectory, which was then input to the grey-box model to ensure a fair comparison of the performance of both MPCs against that of optimal feedback control. An average saving performance of 27.5 % and 23.7 % was achieved using the MPC with grey-box and ML approach over optimal feedback control. Near-optimal performance was achieved with ML approach without running the optimization. The comparable performance of the proposed method implies that the engineering cost in a typical MPC using a grey-box model can be significantly reduced by using the ML method with minimal engineering, which is easy to implement and scalable to other buildings.