Building Energy Optimization Based on Biased ReLU Neural Network
Building Energy Optimization Based on Biased ReLU Neural Network
复制标题
基于偏置ReLU神经网络的建筑能耗优化
DOI：
10.23919/ccc52363.2021.9549947
复制
发表时间：
2021-07
期刊：
影响因子：
--
通讯作者：
Jun Xu
中科院分区：
文献类型：
--
作者：
Hongyi Li;Xinglong Liang;Jun Xu
本文提出了一种基于人工智能技术建模的建筑能源优化策略，首先，通过能量全能的能量消耗仿真软件产生的数据集用作有偏见的Relu神经网络（BRNN）的训练集和测试集。 （MPC）是通过控制建筑物的加热，通风和空调（HVAC）系统来节省能源的，最后，通过引入建筑物控制虚拟测试床（BCVTB）来实现MATLAB和EnergyPlus的关节模拟。
This paper proposes a building energy optimization strategy based on artifical intelligence technology modeling method. Firstly, the data set generated by EnergyPlus energy consumption simulation software is used as the training set and test set of the Biased ReLU neural network (BRNN). Secondly, the building energy consumption prediction model and indoor temperature prediction model are built based on the Biased ReLU neural network. Thirdly, model predictive control (MPC) is uesd to achieve energy saving by controlling the set temperature of the building’s Heating, Ventilation and Air Conditioning (HVAC) system. Finally, the joint simulation of MATLAB and EnergyPlus is realized by introducing the building control virtual test bed (BCVTB). The results show that our method can effectively reduce building energy consumption.