Attaran, S. M. and Yusof, R. and Selamat, H. (2016) A novel optimization algorithm based on epsilon constraint-RBF neural network for tuning PID controller in decoupled HVAC system. Applied Thermal Engineering, 99 . pp. 613-624. ISSN 1359-4311
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Abstract
The energy efficiency of a heating, ventilating and air conditioning (HVAC) system optimized using a radial basis function neural network (RBFNN) combined with the epsilon constraint (EC) method is reported. The new method adopts the advanced algorithm of RBFNN for the HVAC system to estimate the residual errors, increase the control signal and reduce the error results. The objective of this study is to develop and simulate the EC-RBFNN for a self tuning PID controller for a decoupled bilinear HVAC system to control the temperature and relative humidity (RH) produced by the system. A case study indicates that the EC-RBFNN algorithm has a much better accuracy than optimization PID itself and PID-RBFNN, respectively.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Air conditioning, Algorithms, Climate control, Controllers, Electric control equipment, Energy efficiency, Humidity control, Neural networks, Proportional control systems, Radial basis function networks, Three term control systems, Tuning, Decoupled methods, Epsilon constraint, HVAC, Optimization algorithms, PID controllers, Radial basis function neural networks, RBF Neural Network, Temperature and relative humidity, Optimization |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Electrical Engineering |
| ID Code: | 72613 |
| Deposited By: | Haliza Zainal |
| Deposited On: | 27 Nov 2017 04:31 |
| Last Modified: | 27 Nov 2017 04:31 |
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