Application of improved grey prediction model for power load forecasting

Abstract

Although the grey forecasting model has been successfully utilized in many fields, literatures show its performance still could be improved. For this purpose, this paper put forward a GM (1, 1)-connection improved genetic algorithm (GM (1, 1)-IGA) for short- term load forecasting (STLF). While Traditional GM (1, 1) forecasting model is not accurate and the value of parameter a is constant, in order to solve this problem and enhance the accuracy of short-term load forecasting (STLF), the improved decimal-code genetic algorithm (GA) is applied to search the optimal a 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. Finally, a daily load forecasting example is used to test the GM (1, 1)-IGA model and traditional GM (1, 1) model, results show that the GM (1, 1)-IGA had better accuracy and practicality.