Model predictive control strategy using encoder-decoder recurrent neural networks for smart control of thermal environment

The development of deep learning (DL) technology provides an opportunity for accurate prediction and effective control of complex building systems. However, despite the high prediction performance of DL models, few studies integrate DL with the model predictive control (MPC) algorithm for improving building management. In this study, a DL-based MPC framework using encoder-decoder recurrent neural network is developed for realtime control of building thermal environment to exploit the advantage of both DL and MPC algorithms. In addition, to simulate the dynamic interaction between indoor airflow and HVAC systems, a co-simulation platform by integrating HVAC simulator and CFD model is developed to evaluate the proposed building control strategy. Two case studies including a confined space with mixing ventilation and an office room subjected to solar radiation are used for validation of the proposed method. The performance of the DL-based MPC algorithm for building thermal environment control is compared with the traditional proportion-integration-differentiation (PID) controller and the adaptive PID controller. Approximately 4% and 7% energy savings are achieved on average through DL-based MPC compared with adaptive and conventional PID control, respectively. The proposed DL-based MPC framework shows a promising application prospect for building automationby taking the advantage of both deep learning and MPC algorithms.