This paper describes the construction and measured performance of a neural network-based optimal controller for an ice thermal storage system. The controller consists of four neural networks, three of which map equipment behavior and one that acts as a global controller. The controller self-learns equipment responses to the environment and then determines the control settings that should be used. Issues to be addressed are the cost function and selection of a planning window over which the optimization is conducted. The neural network (NN) controller then determines the sequence of control actions that minimize total cost over the planning window. Verification, reported on in a companion paper, is accomplished through computer simulation and on an operational plant.

Units: Dual