Prediction of Energy Consumption Based on Grey Model - GM (1,1)

2012 ◽  
pp. 192-199 ◽  
Author(s):  
Xiuli Yu ◽  
Zhen Lu
Keyword(s):  
Energy Consumption ◽  
Grey Model

scholarly journals Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model

2019 ◽  
Vol 11 (21) ◽  
pp. 5921 ◽  
Author(s):  
Peng Zhang ◽  
Xin Ma ◽  
Kun She
Keyword(s):  
Energy Consumption ◽  
Solar Energy ◽  
Nonlinear Coefficient ◽  
Prediction Method ◽  
Decision Makers ◽  
Grey Model ◽  
Correct Prediction ◽  
Grey Prediction ◽  
Real World Datasets ◽  
Application Fields

Energy consumption is an essential basis for formulating energy policy and programming, especially in the transition of energy consumption structure in a country. Correct prediction of energy consumption can provide effective reference data for decision-makers and planners to achieve sustainable energy development. Grey prediction method is one of the most effective approaches to handle the problem with a small amount of historical data. However, there is still room to improve the prediction performance and enlarge the application fields of the traditional grey model. Nonlinear grey action quantity can effectively improve the performance of the grey prediction model. Therefore, this paper proposes a novel incomplete gamma grey model (IGGM) with a nonlinear grey input over time. The grey input of the IGGM model is a revised incomplete gamma function of time in which the nonlinear coefficient determines the performance of the IGGM model. The WOA algorithm is employed to seek for the optimal incomplete coefficient of the IGGM model. Then, the validations of IGGM are performed on four real-world datasets, and the results exhibit that the IGGM model has more advantages than the other state-of-the-art grey models. Finally, the IGGM model is applied to forecast Japan’s solar energy consumption in the next three years.

scholarly journals A Novel Power-Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Peng Zhang ◽  
Xin Ma ◽  
Kun She
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Nonlinear Parameter ◽  
Whale Optimization Algorithm ◽  
Grey Model ◽  
Living Standard ◽  
Grey Prediction ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Whale Optimization

Along with the improvement of Chinese people’s living standard, the proportion of residential energy consumption in total energy consumption is rapidly increasing in China year by year. Accurately forecasting the residential energy consumption is conducive to making energy programming and supply plan for the administrative departments or energy companies. By improving the grey action quantity of traditional grey model with an exponential time term, a novel power-driven grey model is proposed to forecast energy consumption as reference data for decision makers. The nonlinear parameter of power-driven grey action quantity is a crucial factor to influence the prediction precision. To promote the prediction accuracy of the power-driven grey model, whale optimization algorithm is adopted to seek for the optimal value of the nonlinear parameter. Two validations on real-world datasets are conducted, and the results indicate that the power-driven grey model has significant advantages on the aspect of prediction performance compared with the other seven classical grey prediction methods. Finally, the power-driven grey model is applied in forecasting the total residential energy and the thermal energy consumption of China.

scholarly journals Forecasting Clean Energy Consumption in China by 2025: Using Improved Grey Model GM (1, N)

2020 ◽  
Vol 12 (2) ◽  
pp. 698 ◽  
Author(s):  
Maolin Cheng ◽  
Jiano Li ◽  
Yun Liu ◽  
Bin Liu
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Clean Energy ◽  
Equation Model ◽  
Grey Model ◽  
Population Increase ◽  
Factors Affecting ◽  
Comparison Results ◽  
Conventional Models ◽  
Improved Model

Forecasting China’s clean energy consumption has great significance for China in making sustainably economic development strategies. Because the main factors affecting China’s clean energy consumption are economic scale and population size, and there are three variables in total, this paper tries to simulate and forecast China’s clean energy consumption using the grey model GM (1, 3). However, the conventional grey GM (1, N) model has great simulation and forecasting errors, the main reason for which is the structural inconsistency between the grey differential equation for parameter estimation and the whitening equation for forecasting. In this case, this paper improves the conventional model and provides an improved model GM (1, N). The modeling results show that the improved grey model GM (1, N) built with the method proposed improves simulation and forecasting precision greatly compared with conventional models. To compare the model with other forecasting models, this paper builds a grey GM (1, 1) model, a regression model and a difference equation model. The comparison results show that the improved grey model GM (1, N) built with the method proposed shows simulation and forecasting precision superior to that of other models as a whole. In the final section, the paper forecasts China’s clean energy consumption from 2019 to 2025 using the improved grey model GM (1, N). The forecasting results show that, by 2025, China’s clean energy consumption shall reach the equivalent of 1.504976082 billion tons of standard coal. From 2019 to 2025, clean energy consumption shall increase by 11.32% annually on average, far above the economic growth rate, indicating China’s economic growth shall have a great demand for clean energy in the future. Studies have shown that China’s clean energy consumption shall increase rapidly with economic growth and population increase in the next few years.

scholarly journals Geometrical Variable Weights Buffer GM(1,1) Model and Its Application in Forecasting of China’s Energy Consumption

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Wei Li ◽  
Han Xie
Keyword(s):  
Energy Consumption ◽  
Maximum Degree ◽  
Original Data ◽  
Grey Model ◽  
Variable Weights ◽  
Proposed Model ◽  
Term Energy ◽  
Long Term Forecasting ◽  
The Stability

In order to improve the application area and the prediction accuracy of GM(1,1) model, a novel Grey model is proposed in this paper. To remedy the defects about the applications of traditional Grey model and buffer operators in medium- and long-term forecasting, a Variable Weights Buffer Grey model is proposed. The proposed model integrates the variable weights buffer operator with the background value optimized GM(1,1) model to implement dynamic preprocessing of original data. Taking the maximum degree of Grey incidence between fitting value and actual value as objective function, then the optimal buffer factor is chosen, which can improve forecasting precision, make forecasting results embodying the internal trend of original data to the maximum extent, and improve the stability of the prediction. To verify the effectiveness of the proposed model, the energy consumption in China from 2002 to 2009 is used for the modeling to forecast the energy consumption in China from 2010 to 2020, and the forecasting results prove that the GVGM(1,1) model has remarkably improved the forecasting ability of medium- and long-term energy consumption in China.

Sign in / Sign up

Export Citation Format

Share Document