Prediction Model of Grain Pile Temperature Field Based on Bagging-WOA-SVR
The array distributed temperature measurement cable was used to detect the temperature change of the granary,the machine learning technology was used to predict the grain temperature,and the 1-year monitoring data of the granary was used to predict the temperature of the granary in the next 27 days.The traditional single model of BP,RBF,RF and SVR had the disadvantages of large error and poor generalization ability in predicting the tempera-ture of grain pile.A support vector regression model optimized by whale algorithm based on Bagging ensemble(Bag-ging-WOA-SVR)was proposed and compared with the support vector regression model optimized by grey wolf al-gorithm.A variety of factors affecting the temperature of the granary were analyzed by grey correlation analysis.The temperature inside the granary,the humidity inside the granary,the temperature outside the granary,the humidity outside the granary,the average temperature of the granary,and the surface temperature were selected as the input of the neural network.The average temperature of the granary was used as the prediction output,and three indicators were selected as the evaluation criteria to compare and analyze the prediction accuracy of the model.The results indi-cated that the proposed Bagging-WOA-SVR model had better stability in comparison,with a mean square error of 0.24 and a correlation coefficient of 0.989 2.
grain pile temperatureregression predictionBagging-WOA-SVRprediction model
NETL
NSTL
万方数据
