首页|Studies in the Area of Machine Learning Reported from Nanjing Normal University (Optimal operation strategy predictive control for an integrated radiant cooling with fresh air system based on machine learning)
Studies in the Area of Machine Learning Reported from Nanjing Normal University (Optimal operation strategy predictive control for an integrated radiant cooling with fresh air system based on machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting originating from Nanjing , People’s Republic of China, by NewsRx correspondents, research stated, “Radian t cooling systems are widely valued for their great comfort and energy-saving po tential. However, they still face the risk of condensation in the early stages o f operation, especially in case of random occupancy and intermittent operation.” Our news reporters obtained a quote from the research from Nanjing Normal Univer sity: “This study aims to avoid unacceptable discomfort durations in randomly oc cupied rooms that installed integrated radiant cooling and fresh air system whil e consuming as little energy as possible. This paper firstly compares the effect s of adopting only the setback or standby cooling strategy in a randomly occupie d conference room by simulation. The simulation results demonstrate the necessit y for predicting optimal operation strategy. Subsequently, optimal operation str ategy predictive models were built using three machine learning algorithms on th ree datasets. The evaluation results of the models indicate the feasibility of u sing 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 o peration strategy and achieved good results: discomfort durations of 97.56% of the conferences were within the acceptable range. Additionally, compared to o nly adopting the standby cooling strategy, the radiant cooling system operating time was reduced by 8.88%, and the total energy consumption was red uced by 28.85 kWh.”
Nanjing Normal UniversityNanjingPeop le’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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