摘要
提出利用最大相关和最小冗余(mRMR)算法、粒子群优化(PSO)算法,对BP神经网络预测模型进行优化。对某住宅楼进行供热负荷预测,评价3种神经网络预测模型(BP神经网络预测模型、mRMR-BP神经网络预测模型、PSO-mRMR-BP神经网络预测模型)的预测效果。在3种神经网络预测模型中,BP神经网络预测模型的预测效果最差,PSO-mRMR-BP神经网络预测模型的预测效果最佳。与BP神经网络预测模型相比,经过mRMR算法对输入变量进行筛选以及PSO算法对初始参数进行优化,PSO-mRMR-BP神经网络预测模型的预测效果显著提高。
It is proposed to use the maximum relevance minimum redundancy(mRMR)algorithm and the particle swarm optimization(PSO)algorithm to optimize the BP neural network prediction model.The heating load of a residential building is predicted,and the prediction effects of three neural network prediction models(BP neural network prediction model,mRMR-BP neural network prediction model,and PSO-mRMR-BP neural network prediction model)are evaluated.Among the three neural network prediction models,the BP neural network prediction model has the worst prediction effect,and the PSO-mRMR-BP neural network prediction model has the best prediction effect.Compared with the BP neural network prediction model,through the mRMR algorithm to screen input variables and the PSO algorithm to optimize the initial parameters,the prediction effect of the PSO-mRMR-BP neural network prediction model is significantly improved.
作者
杜润琪
于丹
刘益民
岑悦
DU Runqi;YU Dan;LIU Yimin;CEN Yue
出处
《煤气与热力》
2024年第1期6-9,34,共5页
Gas & Heat
