Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control. (15th April 2021)

• Record Type: Journal Article
• Authors: Yang, Shiyu; Wan, Man Pun; Chen, Wanyu; Ng, Bing Feng; Dubey, Swapnil
• Is Part Of: Applied energy. Volume 288(2021)
• Publication Date: 2021-04-15
• Subjects: Model Predictive Control (MPC) -- Machine-learning (ML) -- Recurrent Neural Network (RNN) -- Air Conditioning and Mechanical Ventilation (ACMV); Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.116648
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Proposed an approximate model predictive control for building energy management. The proposed control is based on a dynamic feedback machine learning model. The proposed control is experimentally implemented in two test buildings. The proposed control achieves up to 52% reduction of cooling energy. The proposed control is more than 100% faster than common model predictive control. Abstract: The adoption of model predictive control (MPC) for building automation and control applications is challenged by the high hardware and software requirements to solve its optimization problem. This study proposes an approximate MPC that mimics the dynamic behaviours of MPC using the recurrent neural network with a structure of nonlinear autoregressive network with exogenous inputs. The approximate MPC is developed by learning from the measured operation data of buildings controlled by MPC, therefore it can produce MPC-like control for buildings without needing to solve the optimization problem, significantly reducing the computation load as compared to MPC. The proposed approximate MPC is implemented in two testbeds, an office and a lecture theatre, to control the air-conditioning systems. The control performance of the approximate MPC is compared to MPC as well as the original reactive control of the two testbeds. The approximate MPC retained most of the energy and thermal comfort performance of MPC in both testbeds. For the office, the MPC and approximate MPC reduced 58.5% and 51.6% of cooling energy consumption, respectively, as compared to the original control. For the lecture theatre, the MPC and approximate MPC reduced 36.7% and 36.2% of cooling energy consumption, respectively, as compared to the original control. Meanwhile, both approximate MPC and MPC significantly improved indoor thermal comfort in the two testbeds as compared to their original control. Despite having minor degradation in control performance the approximate MPC was more than 100 times faster than MPC in generating optimal control commands in each time step.