Application potential analysis of different control strategies for radiant floor cooling systems in office buildings in different climate zones of China

Cui, Mengying; Liu, Jiying; Kim, Moon Keun; Wu, Xiaozhou

doi:10.1016/j.enbuild.2023.112772

Cited by 23 publications

(1 citation statement)

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“…Similar to traditional air conditioning systems, employing suitable operational approaches is crucial for reducing energy consumption in combined radiant floor and fan coil cooling (RFCFC) systems [21]. Cui et al [22] studied the efficiency of four control strategies for radiant cooling systems within office buildings situated across various climatic regions in China. They deduced that an appropriately engineered control system could effectively mitigate the influence of external climatic factors on indoor thermal comfort, which resulted in decreasing carbon emissions and energy usage.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Algorithms in Predicting the Performance of a Combined Radiant Floor and Fan Coil Cooling System

Lu,

Cui,

Gao

et al. 2024

Buildings

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Machine learning algorithms have proven to be practical in a wide range of applications. Many studies have been conducted on the operational energy consumption and thermal comfort of radiant floor systems. This paper conducts a case study in a self-designed experimental setup that combines radiant floor and fan coil cooling (RFCFC) and develops a data monitoring system as a source of historical operational data. Seven machine learning algorithms (extreme learning machine (ELM), convolutional neural network (CNN), genetic algorithm-back propagation (GA-BP), radial basis function (RBF), random forest (RF), support vector machine (SVM), and long short-term memory (LSTM)) were employed to predict the behavior of the RFCFC system. Corresponding prediction models were then developed to evaluate operative temperature (Top) and energy consumption (Eh). The performance of the model was evaluated using five error metrics. The obtained results showed that the RF model had very high performance in predicting Top and Eh, with high correlation coefficients (>0.9915) and low error metrics. Compared with other models, it also demonstrated high accuracy in Eh prediction, yielding maximum reductions of 68.1, 82.4, and 43.2% in the mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE), respectively. A sensitivity ranking algorithm analysis was also conducted. The obtained results demonstrated the importance of adjusting parameters, such as the radiant floor supply water temperature, to enhance the indoor comfort. This study provides a novel and effective method for evaluating the energy efficiency and thermal comfort of radiant cooling systems. It also provides insights for optimizing the efficiency and thermal comfort of RFCFC systems, and lays a theoretical foundation for future studies integrating machine learning algorithms in this field.

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Section: Introductionmentioning

confidence: 99%