An application of genetic programming for power system planning and operation

This work incorporates the identification of model in functional form using curve fitting and genetic programming technique which can forecast present and future load requirement. Approximating an unknown function with sample data is an important practical problem. In order to forecast an unknown function using a finite set of sample data, a function is constructed to fit sample data points. This process is called curve fitting. There are several methods of curve fitting. Interpolation is a special case of curve fitting where an exact fit of the existing data points is expected. Once a model is generated, acceptability of the model must be tested. There are several measures to test the goodness of a model. Sum of absolute difference, mean absolute error, mean absolute percentage error, sum of squares due to error (SSE), mean squared error and root mean squared errors can be used to evaluate models. Minimizing the squares of vertical distance of the points in a curve (SSE) is one of the most widely used method .Two of the methods has been presented namely Curve fitting technique & Genetic Programming and they have been compared based on (SSE)sum of squares due to error.