Dynamic behavior prediction of air conditioning system using Bayesian regularization neural network

2018 ◽  
Vol 2018.28 (0) ◽  
pp. 409
Author(s):  
S. SHOLAHUDIN ◽  
Keisuke OHNO ◽  
Seiichi YAMAGUCHI ◽  
Kiyoshi SAITO
Keyword(s):  
Neural Network ◽  
Dynamic Behavior ◽  
Air Conditioning ◽  
Bayesian Regularization ◽  
Behavior Prediction ◽  
Air Conditioning System ◽  
Bayesian Regularization Neural Network

scholarly journals Thermal comfort control via air conditioning system using fuzzy neural network feedback controller

2020 ◽  
Vol 19 (2) ◽  
pp. 586
Author(s):  
Somaye A. Mohamadi ◽  
Abdulraheem J. Ahmed
Keyword(s):  
Neural Network ◽  
Thermal Comfort ◽  
Environmental Conditions ◽  
Air Conditioning ◽  
Fuzzy Controller ◽  
Environmental Parameters ◽  
Thermal Conditions ◽  
Real Time Control ◽  
Air Conditioning System ◽  
Air Conditioning Systems

<span>Despite their complexity and uncertainty, air conditioning systems should provide the optimal thermal conditions in a building. These controller systems should be adaptable to changes in environmental parameters. In most air conditioning systems, today, there are On/Off controllers or PID in more advanced types, which, due to different environmental conditions, are not optimal and cannot provide the optimal environmental conditions. Controlling thermal comfort of an air conditioning system requires estimation of thermal comfort index. In this study, fuzzy controller was used to provide thermal comfort in an air conditioning system, and neural network was used to estimate thermal comfort in the feedback path of the controller. Fuzzy controller has a good response given the non-linear features of air conditioning systems. In addition, the neural network makes it possible to use thermal comfort feedback in a real-time control.</span>

Neural network air-conditioning system for individual comfort

1991 ◽  
Author(s):  
Toshikazu Takemori ◽  
Nobuji Miyasaka ◽  
Shozo Hirose
Keyword(s):  
Neural Network ◽  
Air Conditioning ◽  
Air Conditioning System

Energy Consumption Forecasting Using United Grey System–Bayesian Regularization Neural Network Model

2012 ◽  
Vol 524-527 ◽  
pp. 3087-3092 ◽  
Author(s):  
Xiao Hui Hu ◽  
Lv Jun Zhan ◽  
Yun Xue ◽  
Gui Xi Liu ◽  
Zhe Fan
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Regularization Parameter ◽  
Prediction Method ◽  
Neural Model ◽  
Bayesian Regularization ◽  
Grey System ◽  
Bayesian Regularization Neural Network ◽  
Energy Consumption Prediction

The energy consumption of the enterprise is subject to various factors. To solve the problem, a new grey-neural model is proposed which effectively combines the grey system and Bayesian-regularization neural network and avoids the disadvantages of each other. The case study indicates that the prediction method is not only reasonable in theory but also owns good application value in the energy consumption prediction. Meanwhile, results also exhibit that G-BRNN model has the automated regularization parameter selection capability and may ensure the excellent adaptability and robustness.

Thermodynamic model of a thermal storage air conditioning system with dynamic behavior

2013 ◽  
Vol 112 ◽  
pp. 160-169 ◽  
Author(s):  
Evan Fleming ◽  
Shaoyi Wen ◽  
Li Shi ◽  
Alexandre K. da Silva
Keyword(s):  
Dynamic Behavior ◽  
Thermodynamic Model ◽  
Air Conditioning ◽  
Thermal Storage ◽  
Air Conditioning System
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