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.
Keywords :
arithmetic; genetic algorithms; grey systems; load forecasting; decimal-code genetic algorithm; grey forecasting model; grey prediction model; one-point linearity arithmetical crossover; power load forecasting; Difference equations; Differential equations; Economic forecasting; Genetic algorithms; Load forecasting; Load modeling; Power generation economics; Power system modeling; Power system reliability; Predictive models; Genetic Algorithm; Grey System; One-point Linearity Arithmetical Crossover; Short-term Load Forecasting;
