首页|Shandong Jianzhu University Researcher Updates Knowledge of Machine Learning (A Comparative Analysis of Machine Learning Algorithms in Predicting the Performanc e of a Combined Radiant Floor and Fan Coil Cooling System)
Shandong Jianzhu University Researcher Updates Knowledge of Machine Learning (A Comparative Analysis of Machine Learning Algorithms in Predicting the Performanc e of a Combined Radiant Floor and Fan Coil Cooling System)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Researchers detail new data in artificial intelli gence. According to news reporting originating from Jinan, People's Republic of China, by NewsRx correspondents, research stated, "Machine learning algorithms h ave proven to be practical in a wide range of applications. Many studies have be en conducted on the operational energy consumption and thermal comfort of radian t floor systems." Funders for this research include Natural Science Foundation of Shandong Provinc e. The news journalists obtained a quote from the research from Shandong Jianzhu Un iversity: "This paper conducts a case study in a self-designed experimental setu p that combines radiant floor and fan coil cooling (RFCFC) and develops a data m onitoring system as a source of historical operational data. Seven machine learn ing algorithms (extreme learning machine (ELM), convolutional neural network (CN N), genetic algorithm-back propagation (GA-BP), radial basis function (RBF), ran dom forest (RF), support vector machine (SVM), and long short-term memory (LSTM) ) were employed to predict the behavior of the RFCFC system. Corresponding predi ction models were then developed to evaluate operative temperature (Top) and ene rgy consumption (Eh). The performance of the model was evaluated using five erro r metrics. The obtained results showed that the RF model had very high performan ce 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 4 3.2 % in the mean absolute percentage error (MAPE), mean squared er ror (MSE), and mean absolute error (MAE), respectively. A sensitivity ranking al gorithm analysis was also conducted."
Shandong Jianzhu UniversityJinanPeop le's Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachin e Learning
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