Real-Time Model Predictive Control of Air-Conditioners Through IoT—Results From an Experimental Setup in a Tropical Climate

There is an increasing demand for air-conditioners (ACs) to maintain a comfortable indoor environment for all types and sizes of buildings including commercial, industrial, and office spaces. Such ACs are mostly operated by traditional ON–OFF controllers to maintain a temperature setpoint. Extensive control engineering knowledge resulting from experiments on actual buildings is needed before the wide application of an advanced control methods, such as model predictive control (MPC), which are more effective and energy-efficient than the traditional controllers. Simulation studies on the application of control may provide promising results, but the corresponding experimental validations may prove otherwise. User-friendly experimental setups to investigate the performance of real-time advanced control on an actual building and its HVAC system is scarce. This paper details the design, implementation and testing of a user-friendly, remote and autonomous test bed to acquire measured data through an IoT platform, and to control ACs in real time through MATLAB Thingspeak. Measurement and data acquisition equipment are installed in a two-zone concrete building in Mauritius. MPC of the indoor air temperature achieved an AC temperature setpoint tracking RMSE which was 0.7°C lower than that achieved by the built-in ON/OFF AC control. The test bed also revealed that portable air-conditioners are not very efficient, given that the maximum cooling efficiency achieved in this work was only 60%. It also provided valuable insights based on the experiments carried out, in terms of improvements to sensing and data acquisition. Control engineers can implement such a test bed for the development and application of advanced controllers as per their needs and applications.