A novel adaptive predictive control strategy of hybrid radiant-air cooling systems operating in desert climates

Hydronic radiant-air cooling systems have proved to be highly energy-efficient compared to all-air systems. However, such systems require advanced control strategies to overcome their slow thermal response and potential water vapor condensation on the radiant surfaces. Yet, there is a clear lack of proposed adaptive controllers in the literature to collaboratively manage both radiant and air sides of the system. Such adaptive controllers are necessary for reliable and stable operation with minimal energy consumption. In this study, a novel offline adaptive predictive control strategy is proposed for the advantages of maximizing the energy savings of hybrid radiant-air systems, located in desert climate zones, while maintaining a higher quality indoor environment. A transient simulation model is developed in TRNSYS v.18 based on a typical medium-office space, located in Cairo, Egypt, with two air-cooled chiller loops serving a thermally activated building system and a dedicated outdoor air system. A multi-input multi-output multi-layered perceptron artificial neural network model has been developed in MATLAB based on batch simulations, performed in TRNSYS, to predict the electricity consumption and Predicted Percentage Dissatisfied (PPD) for different setpoints. The trained model was used as a fitness function of a multi-objective genetic algorithm, coupled with a decision-making criterion, to determine chillers' setpoint temperatures that minimize the electricity consumption and thermal discomfort on a half-hourly simulation basis. The proposed controller was compared to a baseline three-step feedback controller, which turns the water circulation pumps on or off depending on the difference between the setpoint temperature and the indoor air temperature, as well as the initial state of the pumps (on or off). The results show that the proposed strategy substantially improves the indoor thermal environment, where the percentage of comfortable occupied hours is increased by 25.1%. The proposed controller also reduced the peak electricity consumption by 18.8%. However, the total energy consumption of the cooling season was lower by only 2.7% due to the longer operating hours of the chillers and the circulation pumps with this controller. Different strategies for further reduction of total energy consumption with a minimal tuning of the proposed controller are recommended for future studies.