Optimal operation strategy predictive control for an integrated radiant cooling with fresh air system based on machine learning

Abstract

Radiant cooling systems are widely valued for their great comfort and energy-saving potential. However, they still face the risk of condensation in the early stages of operation, especially in case of random occupancy and intermittent operation. This study aims to avoid unacceptable discomfort durations in randomly occupied rooms that installed integrated radiant cooling and fresh air system while consuming as little energy as possible. This paper firstly compares the effects of adopting only the setback or standby cooling strategy in a randomly occupied conference room by simulation. The simulation results demonstrate the necessity for predicting optimal operation strategy. Subsequently, optimal operation strategy predictive models were built using three machine learning algorithms on three datasets. The evaluation results of the models indicate the feasibility of using data from neighbouring cities to improve the generalisation ability of the target city model. Finally, the best one of models was used to predict optimal operation strategy and achieved good results: discomfort durations of 97.56% of the conferences were within the acceptable range. Additionally, compared to only adopting the standby cooling strategy, the radiant cooling system operating time was reduced by 8.88%, and the total energy consumption was reduced by 28.85 kWh. In this study, a model to predict optimal operation strategy is proposed. By simulating and analysing from both comfort and energy perspectives, the optimal operation strategy predictive control method effectively limits the discomfort duration and reduces energy consumption and radiant system runtime. This study provides an example of practical engineering and machine learning applications for radiant cooling systems.