An improved genetic algorithm— GM(1,1) for power load forecasting problem

A mathematical model known as grey model GM(1,1) has been employed successfully in the forecasting of power load system. Because traditional GM (1, 1) forecasting model is not accurate and the value of parameter alpha is constant, so this paper put forward a improved genetic algorithm - GM (1, 1) (IGA-GM (1, 1)), the proposed algorithm were used to solve the problem of short-term load forecasting (STLF) in power system. In order to construct optimal grey model GM (1, 1) to enhance the accuracy of forecasting, the improved decimal-code genetic algorithm (GA) is applied to search the optimal alpha value of grey model GM (1, 1). Whatpsila s more, this paper also proposes the one-point linearity arithmetical crossover, which can greatly improve the speed of crossover and mutation. Then, a comparison of the performance has been made between IGA-GM (1, 1) and traditional GM (1, 1) forecasting model. Finally, a daily load forecasting example is used to test the IGA-GM (1, 1) model. Results show that the IGA-GM (1, 1) had better accuracy and practicality.