Identification of Chiller Model in HVAC System Using Fuzzy Inference Rules with Zadeh’s Implication Operator
In the heating, ventilating, and air-conditioning (HVAC) system, chiller is the central part and one of the primary energy consumers. For the purpose of saving energy, the identification of the chiller model is of great significance. In this paper, based on fuzzy inference rules with Zadeh’s implication operator, the model of chiller in HVAC is identified. The mean square error (MSE) is employed to evaluate the approximating capability of the fuzzy inference system. The objective of the problem is to minimize MSE. Since the Zadeh’s implication operator is adopted in the fuzzy inference, the output of the system becomes a continuous but non-smooth function. In addition, the objective function contains many parameters that need to be optimized, consequently, traditional optimization algorithms based on gradient descent method fail to work. Therefore, an improved genetic algorithm (GA) is applied to minimize the MSE. Actual operational data of a chiller in HVAC are gathered to train the fuzzy inference system. Numerical experiment results validate the accuracy and efficiency of proposed fuzzy model and the improved GA algorithm.
KeywordsChiller fuzzy inference system implication operator improved genetic algorithm
Unable to display preview. Download preview PDF.
- 1.Hodge, B.K., Taylor, R.P.: Analysis and design of energy systems. Prentice Hall, New Jersey (1999)Google Scholar
- 2.Kirsner, W.: Designing for 42°F chilled water supply temperature-—does it save energy? ASHRAE J. 40(1), 37–42 (1998)Google Scholar
- 3.Shelton, S.V., Joyce, C.T.: Cooling tower optimization for centrifugal chillers. ASHRAE J. 33(6), 28–36 (1991)Google Scholar
- 4.Hartman, T.B.: Global optimization strategies for high-performance controls. ASHRAE Trans. 101(2), 679–687 (1995)Google Scholar
- 5.House, J.M., Smith, T.F.: A system approach to optimal control for HVAC and building systems. ASHRAE Trans. 101(2), 647–660 (1995)Google Scholar
- 6.Li, Y.X., Cai, X.B., et al.: Fuzzy control technology for energy saving and its application on HVAC system. China Construction PressGoogle Scholar
- 16.Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
- 17.Spears, W.M., De Jong, K.A.: On the Virtues of Parameterized Uniform Crossover. In: Proc. of the Fourth International Conference on Genetic Algorithms, pp. 230–236 (1991)Google Scholar