Abstract
An optimal neural network-based controller for an ice thermal storage system has been developed and tested. 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 required to minimize operating cost. It has the advantage over other controllers in that it always remains calibrated. Since it does not rely upon rules or assumptions, it is able to provide optimal control under any utility pricing and operating condition. Although originally designed to minimize operating costs, simulation and optimization techniques often determine minimum energy use as well.
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