基于DE-BP神经网络的室内热舒适评价方法  

Evaluation Method of Indoor Thermal Comfort Based on DE-BP Neural Network

作　　者：翁虎 何勇[1] 梁健 WENG Hu;HE Yong;LIANG Jian(College of Computer Science and Technology,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学计算机科学与技术学院,贵阳550025

出　　处：《计算机系统应用》2020年第6期230-234,共5页Computer Systems & Applications

基　　金：贵州大学青年教师科研基金(贵大自青基合字201304)。

摘　　要：本文从智能家居角度研究室内热舒适,分析热舒适评价方式PMV,指出其部分参数在智能家居场景中获取困难.提出在忽略风速和平均辐射温度的情况下,引入气候和环境特征来拟合PMV公式.研究使用经过差分进化算法(Differential Evolution,DE)优化后的BP神经网络算法(DE-BP)来建立拟合模型,DE算法优化神经网络的参数,神经网络训练使用动量加速的随机梯度下降算法,且增加了仿射变换的标准化层和L2正则化.测试结果显示模型在收敛速度、稳定性和泛化性能上比传统BP神经网络更优,在较小误差范围内可应用于计算热舒适度的系统中,降低其输入参量难度.The study researches indoor thermal comfort from the perspective of smart home,analyzes the thermal comfort evaluation method of PMV,and points out that some of its parameters are difficult to obtain in the smart home scene.The study proposes to introduce the climatic and environmental characteristics to fit the PMV formula while ignoring wind speed and average radiant temperature.The research uses BP neural network algorithm optimized by Differential Evolution(DE-BP)to establish a fitting model,DE algorithm optimizes parameters of neural network,neural network training uses momentum-accelerated stochastic gradient descent algorithm,and adds the normalization layer and L2 regularization of the affine transformation.The test results show that the model is better than the traditional BP neural network in terms of convergence speed,stability,and generalization performance,and can be used within a small error range.It is applied to the system for calculating thermal comfort and reduces the difficulty of input parameters.

关 键 词：智能家居 PMV 热舒适 DE-BP神经网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TU111[自动化与计算机技术—控制科学与工程]