The system identification of HVAC artificial neural network
An air conditioner or AC is an apparatus that designed to adjust the temperature as well as humidity in house. A multi-functional air conditioning system which contains functions like heating, ventilation and air conditioning is referred to as “HVAC”. In this study, the purpose is to estimate the dy...
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2012
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my-utm-ep.326342017-08-24T06:27:30Z The system identification of HVAC artificial neural network 2012 Li, Jia Wei T Technology (General) An air conditioner or AC is an apparatus that designed to adjust the temperature as well as humidity in house. A multi-functional air conditioning system which contains functions like heating, ventilation and air conditioning is referred to as “HVAC”. In this study, the purpose is to estimate the dynamic model of the HVAC system by using the Least Square (LS), Recursive Least Square (RLS) and Artificial Neural Network (ANN) techniques. The input and output data used to estimate the dynamic model in this study were obtained experimentally by previous studies. The system identification techniques were conducted based on single-input-single-output (SISO) autoregressive with exogenous (ARX) model structure. The validity of the models was investigated based on mean square error (MSE), regression and correlation tests. The results of every techniques are compared with their performance of identification the system. It is indicating that in this study, the RLS method shows the better results than LS method, however in the methods of system identification using ANN, the time-series structured the method, such as Elman Network give the best results. 2012 Thesis http://eprints.utm.my/id/eprint/32634/ http://eprints.utm.my/id/eprint/32634/1/LiJiaWeiMFKM2012.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:78806?queryType=vitalDismax&query=The+system+identification+of+HVAC+artificial+neural+network&public=true masters Universiti Teknologi Malaysia, Faculty of Mechanical Engineering Faculty of Mechanical Engineering |
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Universiti Teknologi Malaysia |
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UTM Institutional Repository |
| language |
English |
| topic |
T Technology (General) |
| spellingShingle |
T Technology (General) Li, Jia Wei The system identification of HVAC artificial neural network |
| description |
An air conditioner or AC is an apparatus that designed to adjust the temperature as well as humidity in house. A multi-functional air conditioning system which contains functions like heating, ventilation and air conditioning is referred to as “HVAC”. In this study, the purpose is to estimate the dynamic model of the HVAC system by using the Least Square (LS), Recursive Least Square (RLS) and Artificial Neural Network (ANN) techniques. The input and output data used to estimate the dynamic model in this study were obtained experimentally by previous studies. The system identification techniques were conducted based on single-input-single-output (SISO) autoregressive with exogenous (ARX) model structure. The validity of the models was investigated based on mean square error (MSE), regression and correlation tests. The results of every techniques are compared with their performance of identification the system. It is indicating that in this study, the RLS method shows the better results than LS method, however in the methods of system identification using ANN, the time-series structured the method, such as Elman Network give the best results. |
| format |
Thesis |
| qualification_level |
Master's degree |
| author |
Li, Jia Wei |
| author_facet |
Li, Jia Wei |
| author_sort |
Li, Jia Wei |
| title |
The system identification of HVAC artificial neural network |
| title_short |
The system identification of HVAC artificial neural network |
| title_full |
The system identification of HVAC artificial neural network |
| title_fullStr |
The system identification of HVAC artificial neural network |
| title_full_unstemmed |
The system identification of HVAC artificial neural network |
| title_sort |
system identification of hvac artificial neural network |
| granting_institution |
Universiti Teknologi Malaysia, Faculty of Mechanical Engineering |
| granting_department |
Faculty of Mechanical Engineering |
| publishDate |
2012 |
| url |
http://eprints.utm.my/id/eprint/32634/1/LiJiaWeiMFKM2012.pdf |
| _version_ |
1747816050063310848 |