Neural network predictive control of thermal environment at the end of air conditioning system

2021 ◽  
Author(s):  
Gang Li ◽  
Xunhui Huang ◽  
Bin He ◽  
Ping Lu ◽  
Zhipeng Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Air Conditioning ◽  
Thermal Environment ◽  
Air Conditioning System ◽  
Neural Network Predictive Control

ChemInform Abstract: Thermal Stress Management of a Solid Oxide Fuel Cell Using Neural Network Predictive Control

ChemInform ◽  
2014 ◽  
Vol 45 (30) ◽  
pp. no-no
Author(s):  
S. A. Hajimolana ◽  
S. M. Tonekabonimoghadam ◽  
M. A. Hussain ◽  
M. H. Chakrabarti ◽  
N. S. Jayakumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Stress ◽  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Stress Management ◽  
Predictive Control ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Neural Network Predictive Control

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>

scholarly journals Economic Multiple Model Predictive Control for HVAC Systems - A Case Study for a Food Manufacturer in Germany

2018 ◽  
Author(s):  
Tobias Heidrich ◽  
Jonathan Grobe ◽  
Henning Meschede ◽  
Jens Hesselbach
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Air Conditioning ◽  
Variable Temperature ◽  
Conveyor Belt ◽  
Electricity Price ◽  
Multiple Model ◽  
Air Conditioning System ◽  
Discrete State ◽  
Temperature And Humidity

The following paper describes an economical, multiple model predictive control (EMMPC) for an air conditioning system of a confectionery manufacturer in Germany. The application consists of a packaging hall for chocolate bars, in which a new local conveyor belt air conditioning system is used and thus the temperature and humidity limits in the hall can be significantly extended. The EMMPC calculates the optimum energy or cost humidity and temperature set points in the hall. For this purpose, time-discrete state space models and an economic objective function with which it is possible to react to flexible electricity prices in a cost-optimised manner are created. A possible future electricity price model for Germany with a flexible EEG levy was used as a flexible electricity price. The flexibility potential is determined by variable temperature and humidity limits in the hall, which are oriented towards the comfort field for easily working persons, and the building mass. The building mass of the created room model is used as a thermal energy store. Considering electricity price and weather forecasts as well as internal, production plan-dependent load forecasts, the model predictive controller directly controls the heating and cooling register and the humidifier of the air conditioning system.

NEURAL NETWORK PREDICTIVE CONTROL IN A NATURALLY VENTILATED AND FOG COOLED GREENHOUSE

2012 ◽  
pp. 45-52 ◽  
Author(s):  
E. Fitz-Rodríguez ◽  
M. Kacira ◽  
F. Villarreal-Guerrero ◽  
G.A. Giacomelli ◽  
R. Linker ◽  
...  
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Neural Network Predictive Control
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