  In this paper an adaptive load management system that uses predictive control
optimization is introduced. This price elastic system is able to optimize the
consumption of power and is fully autonomous and responsive to market clearing
prices. The area of application chosen was an air-conditioning system that
allows the end user to select a comfort zone that serves as the boundary
conditions for the optimization algorithm. The temperature function that
governs our algorithm is also derived and tested. Numerical examples are then
presented to show the effectiveness of this system on day-ahead and real-time
data from ISOs. The developed system showed promising results and savings that
will improve the utilization of present energy resources. Finally, the
implementation of this system was discussed and some preliminary modeling was
performed to show the potential realization of such a system
