Temperature Prediction of RCC Based on Partial Least-Squares Regression

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• 作者：Zhao Yu-qing ; Yan-liang
• 关键词：RCC ; Multiple linear regression ; partial least-squares regressio ; temperature prediction
• 刊名：Energy Procedia
• 出版年：2012
• 期刊代码：P136_18766102
• 类别：pw
• 出版时间：2012
• 卷：17
• 期：part_PA
• 页码：326-332
• 文件大小：141 K

摘要

the mathematical modeling principle and method of partial least-squares regression is elaborated, based on the temperature monitoring data of some gravity dam, the trend of dam concrete temperature is predicted by partial least-squares regression model. Multiple correlation between Independent variables is overcomed, organic combine on Multiple linear regression,Multiple linear regression and Canonical Correlation Analysis is achieved. Compared with general least-squares regression model result,it has more advanced computing, more accurate result, more practical explanation. it is proved feasible and practical,can be used to predict the concrete temperature, by forecasting concrete temperature of dam, it is known that rock temperature is the most important factor which affects concrete temperature of dam. concrete temperature of dam is decreasing together with rock temperature. Suggestion is proposed that rock temperature should be monitored as emphasis in the future, some scientific basis is provided for temperature control and preventing crack of the dam.