| URN |
etd-0719116-231818 |
Statistics |
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|
| Author |
Tzai-Cheng Hsieh |
| Author's Email Address |
kevin16888888@gmail.com |
| Department |
Department of mechanical engineering |
| Year |
2015 |
Semester |
2 |
| Degree |
Master |
Type of Document |
Master's Thesis |
| Language |
zh-TW.Big5 Chinese |
Page Count |
65 |
| Title |
The artificial neural networks applied to temperature control for air conditioning systems |
| Keyword |
Energy saving controls.
Artificial neural networks
Variable- frequency drive
Variable refrigerant flow
Variable refrigerant flow
Variable- frequency drive
Artificial neural networks
Energy saving controls.
|
| Abstract |
The air conditioning systems apply variable refrigerant flow (VRF) techniques for Energy saving, which also called VRF air conditioning systems. The VRF air conditioning systems can change the refrigerant flow by changing the compressor speed, and usually applied to home and small office space. The modern VRF air conditioning systems applied variable speed compressors with nonlinear characteristics, and the varied room space and refrigerating load requirements lead the PID control becomes insufficient for modern VRF air conditioning systems. This study utilizes the neural network and PI hybrid controls applied to VRF air conditioner system for room temperature control, which can enhance the adaptability and saving energy. The simulation results reveal that the proposed control system can enhance the adaptability and be available with better energy saving performance than PI control. |
| Advisor Committee |
Ming-Huei Chu - advisor
BO-QUAN HUANG - co-chair
ZHENG-GUANG HUANG - co-chair
|
| Files |
indicate access worldwide |
| Date of Defense |
2016-07-11 |
Date of Submission |
2016-07-21 |
