The Development of Cloud-based Building Automation System and Creating Predictive Models of HVAC System with Machine Learning

被引：1
|
作者：
Matsuda, Yuki [1 ]
Ooka, Ryozo [2 ]
Ikeda, Shintaro [3 ]
机构：
[1] DAI DAN Co Ltd, Tech Res Lab, Saitama, Japan
[2] Univ Tokyo, Inst Ind Sci, Tokyo, Japan
[3] Tokyo Inst Technol, Technol & Innovat Management, Tokyo, Japan
来源：
2021 29TH MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION (MED) | 2021年
关键词：
Internet of Things (IoT); Building Automation System (BAS); Neural network; Machine learning; Predictive model;
D O I：
10.1109/MED51440.2021.9480357
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A cloud-based building automation system was developed, and implemented in an actual building featuring a thermo active building system, ground source heat pump system, and battery. Based on the operation and measurement data stored by this system, a predictive model was created using a neural network. In the hyper-parameters of the neural network, the number of hidden layers, nodes in each hidden layer, and history levels of the input data were optimized by particle swarm optimization with the mutation method. Models that predict future behavior of heating, ventilation, and air-conditioning system were evaluated according to normalized mean absolute error, with the best model obtaining 0.0796.
引用
收藏
页码：955 / 960
页数：6
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