Parameter estimation for multiresponse nonlinear chilled-water plant models
- Air Force Inst. of Tech., Wright-Patterson AFB, OH (United States)
- Univ. of Wisconsin, Madison, WI (United States). Solar Energy Lab.
Development of optimal control strategies for complex heating, ventilating, and air-conditioning (HVAC) systems often requires a model of the system. Models of conventional heating or cooling plant equipment are nonlinear with multiple input and output variables. Model parameter values (e.g., an overall heat transfer coefficient, UA, in a heat exchanger model) need to be assigned so that the model adequately predicts the performance of the actual system. Parameters for a model of an operating system can be determined with regression techniques using measurements of model variables. The work presented in this paper considers the multiresponse nonlinear parameter estimation problem associated with a system of interconnected components in a chilled-water plant. Methods studied for solving the estimation problem include ordinary least squares, weighted least squares, and a determinant criterion derived from Bayesian estimation theory. Potential pitfalls in multivariate regression, such as linear dependencies among responses, are identified. The application and analysis of the parameter estimation methods are directed toward building a predictive model for use in optimal supervisory control strategies. Various solution methods are reviewed and applied to a simulated chilled-water plant for comparison and analysis. The parameter estimation techniques are then used to create a predictive model of an operational chilled-water plant. The plant has both electric and steam-driven chillers and cooling towers with multispeed fans. The goal of the predictive model is to predict variables associated with operational costs such as electric motor power and steam consumption. The relative merits of the different regression techniques are judged (compared) according to how well the model can predict these cost-related variables.
- OSTI ID:
- 345253
- Report Number(s):
- CONF-9702141--
- Country of Publication:
- United States
- Language:
- English
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