Applying the Grey Forecasting Model to the Energy Supply Management Engineering

Abstract

The demand for energy supply has been increasing dramatically in recent years in the global. In addition, owing to the uncertain economic structure of the county, energy has a chaotic and nonlinear trend. In this paper, An improved grey G(1,1) prediction model is proposed to the energy management engineering. It is one approach that can be used to construct a model with limited samples to provide better forecasting advantage for long-term problems. The forecasting performance of the improved GM(1,1) model has been confirmed using the China's energy database. And the results, compared with those from artificial neural network (ANN) and times series. According to the experimental results, our proposed new method obviously can improve the prediction accuracy of the original grey model.