摘要
为了改善室内办公场所的热舒适环境和减少能源消耗,应用计算流体力学(CFD)软件建立实验室研究模型,通过CFD实验模型数据训练人工神经网络,并用神经网络模型代替CFD模型.建立遗传算法目标函数,通过调整权重得到不同的优化结果.在保证精度的情况下减少遗传算法的计算量,最终得到室内控制参数的最优解.与直接在CFD模型上应用遗传算法相比,明显减少了计算量.通过修改室内空调送风口的布局和空调控制参数,使室内大部分工作人员获得满意的舒适度.实验结果表明,所选择的目标函数以及优化方法可以使室内工作环境的舒适度得到较大改善,并达到了节能的目的.
In order to improve thermal environment of office and decrease energy costs,office model is built with computational fluid dynamics（CFD） software,artificial neural network is trained with data from CFD model,and CFD model is surrogated by neural network model.Objective function of the algorithm is established and weighting factors in the objective function adjusted to obtain different optimization results.Optimal solution of indoor control variables are obtained when computational cost is decreased without loss of precision.Time-consuming computation of the algorithm proposed is greatly reduced compared with the algorithm to CFD model.Most of indoor personnels can feel comfortable by modifying distribution of outlets of air-conditioner and control variables.The experimental results indicated that the present choice of objective function and optimization approach are able to obviously improve the thermal comfort in indoor environment,and the energy cost is decreased accordingly.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2010年第2期240-244,共5页
Transactions of Beijing Institute of Technology
基金
北京理工大学基础研究基金(20070542009)
关键词
室内热舒适
神经网络
遗传算法
indoor thermal comfort
neural network
genetic algorithm
