Abstract :
In this paper, a GM (1, 1)-connection improved genetic algorithm (GM (1, 1)-IGA) is put forward to solve the problem of short-term load forecasting (STLF) in power system. While Traditional GM (1, 1) forecasting model is not accurate and the value of parameter OC is constant, the proposed algorithm could overcome these disadvantages. 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 OC value of grey model GM (1, 1). What´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 GM (1, 1)-IGA and traditional GM (1, 1) forecasting model. Finally, a daily load forecasting example is used to test the GM (1, 1)-IGA model. Results show that the GM (1, 1)-IGA had better accuracy and practicality.
Keywords :
genetic algorithms; grey systems; load forecasting; decimal-code genetic algorithm; one-point linearity arithmetical crossover; optimal grey model; power system; short-term load forecasting; Difference equations; Differential equations; Economic forecasting; Genetic algorithms; Genetic mutations; Linearity; Load forecasting; Power system modeling; Predictive models; Weather forecasting;
