摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-研究人员详细介绍了人工智能中的新数据。根据NewsRx记者从中国济南发回的新闻报道，研究表明：“机器学习算法已经被证明在广泛的应用中是可行的。人们已经对弧度t地板系统的运行能耗和热舒适性进行了许多研究。”本文在自行设计的地板辐射与风机盘管冷却系统(RFCFC)中进行了案例研究,开发了一套数据监测系统,作为历史运行数据的来源。七种机器学习算法(极限学习机)(ELM)，卷积神经网络(CN)，遗传算法-反向传播(GA-BP)，径向基函数(RBF)，RAN DOM森林(RF)，支持向量机(SVM)，采用长短期记忆(LSTM)(lg-short-memory(LSTM))对RFCFC系统的行为进行了预测,建立了相应的预测模型,并用5个误差指标对模型的性能进行了评价,结果表明,RF模型在预测Top和Eh方面具有很高的性能。与其它模型相比,该模型具有较高的相关系数(>0.9915)和较低的误差度量,平均绝对百分比误差(MAPE)、平均平方误差(MSE)和平均绝对误差(MAE)分别降低了68.1%、82.4%和43.2%,并进行了敏感性排序算法分析。
Abstract
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."
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