Considers the multiresponse non-linear 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 compared according to how well the model can predict these cost-related variables.

KEYWORDS: year 1997, Calculating, chilled water supply, comparing, steam, flow rate, costs, fluid flow, optimisation, energy consumption, performance, computer programs, accuracy, designing