Recently, neural networks and other advanced statistical modeling techniques have gained considerable popularity in the process industries. For example, several utilities are now using neural networks for load prediction and for emissions monitoring. While this technology promises to furnish the modeling needs for the power industry, the full potential of the technology is not being realized. The key limitation is the lack of a systematic design methodology. High-performing models currently require considerable investment of time and expertise.
Neural networks pose an especially complex design problem.
The choice of input variables, network architecture, learning algorithm parameters, can all substantially influence the performance of the network. Furthermore, performance criteria are inevitably application-specific. To demonstrate how high-performance neural network models can automatically be developed for challenging problems relevant to the electric power industry. Thus there is a need for a new type of modeling tool, tailored for applications of interest to the electric power industry and readily usable by utility personnel. Such a tool must minimize the myriad design choices faced by the model developer today, without compromising model fidelity.