Prediction Accuracy Of Grey Model Research Articles

Forecasting the non-renewable energy consumption by an adjacent accumulation grey model

This paper proposes an adjacent accumulation discrete grey model to enhance the prediction accuracy of grey model and improve the utilization of new data. The stability of the proposed model is proved and the new model can obtain higher prediction accuracy through three cases. And it was applied to the prediction of the non-renewable energy consumption in Asia-Pacific Economic Cooperation. It proves that discrete grey model with adjacent accumulation is effective. According to the forecasting results, the oil consumptions in 15 countries have an increasing trend. The coal consumptions of five countries (South Korea, Indonesia, Vietnam, Malaysia and the Philippines) show a clear growth trend and The United States, Canada and Australia all show a downward trend. The consumptions of natural gas in the United States, Mexico, Singapore, China, Vietnam and Peru will all increase. For nuclear energy, China’s consumption will increase and South Korea’s consumption will decrease.

Forecasting Natural Gas Consumption of China Using a Novel Grey Model

As is known, natural gas consumption has been acted as an extremely important role in energy market of China, and this paper is to present a novel grey model which is based on the optimized nonhomogeneous grey model (ONGM (1,1)) in order to accurately predict natural gas consumption. This study begins with proving that prediction results are independent of the first entry of original series using the product theory of determinant; on this basis, it is a reliable approach by inserting an arbitrary number in front of the first entry of original series to extract messages, which has been proved that it is an appreciable approach to increase prediction accuracy of the traditional grey model in the earlier literature. An empirical example often appeared in testing for prediction accuracy of the grey model is utilized to demonstrate the effectiveness of the proposed model; the numerical results indicate that the proposed model has a better prediction performance than other commonly used grey models. Finally, the proposed model is applied to predict China’s natural gas consumption from 2019 to 2023 in order to provide some valuable information for energy sectors and related enterprises.

Prediction of hydroelectric power generation in Japan

ABSTRACTThe prediction model for power systems based on grey model has been studied by many researches. However, the prediction accuracy of grey model is unsatisfying when original data shows great randomness. In this article, in order to improve the prediction capability of grey model, the regression model is first integrated into GM(1, 1) through compensation for the residual error series. Then Markov chain model is applied for achieving the high prediction accuracy. A real case of hydroelectric power generation in Japan is used to validate the effectiveness of the proposed model. Based on our prediction results for hydroelectric power generation in Japan by year from 2010 to 2015, the growth of hydroelectric power generation has not seen a very big change.

Wind Speed Prediction Based on Improved Grey Neural Network Model

Wind speed forecasting is an effective method to improve power stability of wind farm. Grey system theory have certain advantages in the study of poor information and uncertainty problems, it is suitable for the system with limited computing power and data storage capacity, such as wind turbine control system. In order to further improve the prediction accuracy of grey model, we combined GM (1, 1) model and BP neural network prediction model in this paper, and improved the combined model by background value optimizing and introducing genetic algorithm. Through analyzing the simulation results and comparing the forecasting results with the actual wind speed, it is clear that the improved combined prediction model is superior to pure grey forecasting model and it meets the needs of the wind power control.

An optimal hybrid model for atomic power generation prediction in Japan

The grey model GM(1,1), which is based on grey system theory, has become a powerful tool for the prediction problems in power systems. However, the prediction accuracy of grey model is unsatisfying when original data set shows great randomness. In this paper, in order to improve the prediction capability of grey model, the exponential smoothing (ES) method is integrated into GM(1,1) through the preprocessing for original data set. We call the proposed model as ESGM(1,1). The Taylor approximation method is then presented to find the optimal coefficient values of ESGM(1,1). The improved model is defined as T-ESGM(1,1). Finally, Markov chain model is applied to T-ESGM(1,1) for achieving the high prediction accuracy. We call the proposed model as MC-T-ESGM(1,1). A real case of atomic power generation in Japan is used to validate the effectiveness of proposed model.