基于随机森林算法的粮堆机械通风温度预测及控制研究

Research on Prediction and Control of Mechanical Ventilation Temperature of Grain Pile Based on Random Forest Algorithm

为了研究粮堆机械通风温度与其影响因素之间的高维非线性关系,运用随机森林算法建立了预测模型,并用实仓实验数据验证模型的有效性,真实值与预测值的对比表明构建的随机森林模型预测精度较高。为了进一步说明随机森林模型的准确性和可靠性,将预测结果与支持向量机和BP神经网络模型对比。结果显示,随机森林预测模型的误差最小,回归拟合效果最优,可以应用于粮堆机械通风温度的预测。在此基础上,计算出了易调节因素的变化率与粮堆平均温度变化率之间的定量关系,以及粮堆平均温度达到低温条件的临界点集合,为科学地判断粮堆通风时机和温度的控制提供参考。

In order to study the high-dimensional non-linear relationship between the mechanical temperature of the grain pile and its influence factors,the prediction model was established with a random forest algorithm,whose validity was verified by the actual experimental data.By comparing the real value with the predicted value,the results showed that the random forest model had higher prediction accuracy.In order to further demonstrate the accuracy and reliability of the model,the prediction results of the random forest model were compared with the prediction results of the Support Vector Machine and the BP Neural Network model,indicating that the error of the random forest prediction model was the smallest and the regression fitting was the best.Therefore,the model could be applied to predict the mechanical ventilation temperature of grain piles.On this basis,the quantitative relationship between the rate of adjustable factors and the average temperature change rate of the grain pile was calculated,and the critical points set of average grain pile at low temperature condition was calculated,which provided reference for scientifically judging the ventilation timing and temperature control of grain pile.