| URN |
etd-0717121-155441 |
Statistics |
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|
| Author |
Tsung-Sheng Cheng |
| Author's Email Address |
No Public. |
| Department |
Institute Of Mechanical Engineering |
| Year |
2020 |
Semester |
2 |
| Degree |
Master |
Type of Document |
Master's Thesis |
| Language |
zh-TW.Big5 Chinese |
Page Count |
74 |
| Title |
The Artificial Neural Network Applied to Temperature Control for Room Air Conditioning Systems |
| Keyword |
artificial neural networks
variable refrigerant flow
energy saving controls
Variable frequency air conditioning system
Variable frequency air conditioning system
energy saving controls
variable refrigerant flow
artificial neural networks
|
| Abstract |
The modern room air conditioning systems apply variable frequency driver techniques for Energy saving, which also called variable refrigerant flow (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 controls applied to VRF air conditioner system for room temperature control, which can enhance the adaptability and saving energy. The proposed neural network has three layers, and the activation functions of hidden and output layers outputs are sigmoid functions. The simulation results reveal that the proposed neural control system can enhance the adaptability, and be available to room temperature control with better energy saving performance than conventional PID control. |
| Advisor Committee |
Ming-Huei Chu - advisor
|
| Files |
indicate access worldwide |
| Date of Defense |
2021-07-09 |
Date of Submission |
2021-07-17 |
