摘要
为了提高城市建筑能源管理的效率,从而实现节能减排目的,提出了一种基于小波神经网络的建筑BIM能耗预测算法。该方法首先根据限制因素建立了标准的建筑模型。然后根据简化原则,以某商务型公寓楼为例通过BIM技术对建筑模型进行了参数化。最后运用BP小波神经网络对模型能耗进行预测算。仿真实验结果显示,提出方法的预测误差在合理范围内,验证了其可行性。
In order to improve the efficiency of urban building energy management and achieve energy saving and emission reduction,a building BIM energy consumption prediction algorithm based on wavelet neural network is proposed.This method first establishes a standard building model based on the constraints.Then,based on the principle of simplification,the BIM technology was used to parameterize the building model by using a commercial apartment building as an example.Finally,using BP wavelet neural network to predict the model energy consumption.Simulation results show that the prediction error of the proposed method is within a reasonable range, and its feasibility is verified.
作者
张先锋
Xian-feng ZHANG(School of Economics &Management,China University of Geosciences ,Wuhan 430030,China)
出处
《机床与液压》
北大核心
2018年第24期42-47,93,共7页
Machine Tool & Hydraulics
基金
The Research for Project Management Teachers on Construction Project Practice Ability in BIM(2017A26)~~
关键词
建筑
节能减排
能耗预测
模型参数
BIM
小波神经网络
Buildings
Energy saving and emission reduction
Energy consumption prediction
Model parameters
BIM
Wavelet neural network
