scholarly journals A Novel Extrapolation-Based Grey Prediction Model for Forecasting China’s Total Electricity Consumption

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin-bo Yang
Keyword(s):  
Prediction Model ◽  
Extreme Values ◽  
Model Performance ◽  
Electricity Consumption ◽  
The Novel ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Background Value ◽  
Development Policies ◽  
The Government

Accurately forecasting China’s total electricity consumption is of great significance for the government in formulating sustainable economic development policies, especially, China as the largest total electricity consumption country in the world. The calculation method of the background value of the GM(1, 1) model is an important factor of unstable model performance. In this paper, an extrapolation method with variable weights was used for calculating the background value to eliminate the influence of the extreme values on the performance of the GM(1, 1) model, and the novel extrapolation-based grey prediction model called NEGM(1, 1) was proposed and optimized. The NEGM(1, 1) model was then used to simulate the total electricity consumption in China and found to outperform other grey models. Finally, the total electricity consumption of China from 2018 to 2025 was forecasted. The results show that China’s total electricity consumption will be expected to increase slightly, but the total is still very large. For this, some corresponding recommendations to ensure the effective supply of electricity in China are suggested.

Forecasting China's electricity consumption using a new grey prediction model

Energy ◽  
2018 ◽  
Vol 149 ◽  
pp. 314-328 ◽  
Author(s):  
Song Ding ◽  
Keith W. Hipel ◽  
Yao-guo Dang
Keyword(s):  
Prediction Model ◽  
Electricity Consumption ◽  
Grey Prediction ◽  
Grey Prediction Model

scholarly journals Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaoshuang Luo ◽  
Bo Zeng ◽  
Hui Li ◽  
Wenhao Zhou
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Prediction Models ◽  
Grey Model ◽  
The Novel ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Combination Optimization ◽  
Installed Capacity ◽  
Novel Model

The intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable solution to meet this challenge. However, the single parameter optimization of the conventional grey model has some limitations in improving its modeling ability. To this end, a novel grey prediction model with parameters combination optimization is proposed in this paper. Firstly, considering the modeling mechanism and process, the order of accumulation generation of the grey prediction model is optimized by Particle Swarm Optimization (PSO) Algorithm. Secondly, as different orders of accumulation generation correspond to different parameter matrixes, the background value coefficient of the grey prediction model is optimized based on the optimal accumulation order. Finally, the novel model of combinational optimization is employed to simulate and forecast Chinese WPIC, and the comprehensive error of the novel model is only 1.34%, which is superior to the other three grey prediction models (2.82%, 1.68%, and 2.60%, respectively). The forecast shows that China’s WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the standpoint of the practitioners and governments.

Analysing the high-tech industry with a multivariable grey forecasting model based on fractional order accumulation

Kybernetes ◽  
2019 ◽  
Vol 48 (6) ◽  
pp. 1158-1174 ◽  
Author(s):  
Liang Zeng
Keyword(s):  
Prediction Model ◽  
Fractional Order ◽  
Guangdong Province ◽  
High Tech ◽  
Grey Prediction ◽  
Content Type ◽  
Grey Prediction Model ◽  
Background Value ◽  
Grey Forecasting ◽  
Optimized Model

PurposeHigh-tech industries play an important role in promoting economic and social development. The purpose of this paper is to accurately predict and analyze the output value of high-tech products in Guangdong Province, China, by using a multivariable grey model.Design/methodology/approachBased on the principle of fractional order accumulation, this study proposes a multivariable grey prediction model. To further enhance the prediction ability and accuracy of the model, an optimized model is established by reconstructing the background value. The optimal parameters are solved by minimizing the average relative error of the system characteristic sequence with the constraint of parameter relationships.FindingsThe results from the study show that the two proposed models exhibit better simulation and prediction performance than the traditional models, while the optimized model can significantly improve the modelling precision. In addition, it is predicted that the output value of high-tech products is 12,269.443bn yuan in 2021, which will approximately double from 2016 to 2021.Research limitations/implicationsThe two proposed models can be used to forecast the trend of the system and are grown as an effective extension and supplement of the traditional multivariable grey forecasting models.Practical implicationsThe forecast and analysis of the development prospects of high-tech industries would be useful for the government departments of Guangdong Province and professional forecasters to grasp the future of high-tech industries and formulate decision planning.Originality/valueA new multivariable grey prediction model based on fractional order accumulation and its optimized model obtained by reconstructing the background value, which can improve the modelling accuracy of the traditional model, is proposed in this paper.

Prediction and optimization of chemical fiber spinning tension based on grey system theory

2018 ◽  
Vol 89 (15) ◽  
pp. 3067-3079 ◽  
Author(s):  
Qihong Zhou ◽  
Tianlun Wei ◽  
Yiping Qiu ◽  
Fangmin Tang ◽  
Lixin Yin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Process Parameters ◽  
Prediction Accuracy ◽  
Fiber Spinning ◽  
Chemical Fiber ◽  
Grey Prediction ◽  
Modeling Error ◽  
Grey Prediction Model ◽  
Background Value ◽  
Winding Tension

Based on the grey prediction model, this paper studied the effect of the chemical fiber spinning process parameters on the winding tension. Suitable process parameters were selected to carry out grey incidence analysis with winding tension, and the feasibility of the grey prediction model in spinning tension prediction was validated by the designed experiments. The corresponding algorithm routines of various grey prediction models were written in MATLAB. The single-variable grey prediction model of GM(1,1) showed a higher prediction accuracy in the effect of the single process parameter changing on spinning tension; when multiple process parameters changed at the same time, the average modeling error of the MGM(1, n) multi-variable grey prediction model was 7.70%, and the maximum error was as high as 32.99%. The original MGM(1, n) model was optimized and the model background value was adjusted by using the auto-optimization and weighting method. The average modeling error of the improved model was reduced to 2.02%, which could meet the general accuracy requirement of tension prediction. Further combining fractional-order accumulation and adjusting the background value coefficient α and the cumulative order r jointly, the smallest prediction error was found among the 100,000 combinations, and the final error was further reduced to 1.30%. The results show that the grey prediction model is suitable and effective for predicting the spinning tension based on the process parameters. Appropriate model improvement mechanisms will increase the prediction accuracy significantly. This application provides a suitable method for spinning tension prediction, which has great significance for the tension control of chemical fiber products.

A multiperiod grey prediction model and its application

2021 ◽  
pp. 1-10
Author(s):  
D. Luo ◽  
G.Z. Zhang
Keyword(s):  
Fourier Series ◽  
Prediction Model ◽  
Mean Square Error ◽  
Loss Rate ◽  
Minimum Mean Square Error ◽  
The Novel ◽  
Mean Square ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Novel Model

The purpose of this paper is to solve the prediction problem of nonlinear sequences with multiperiodic features, and a multiperiod grey prediction model based on grey theory and Fourier series is established. For nonlinear sequences with both trend and periodic features, the empirical mode decomposition method is used to decompose the sequences into several periodic terms and a trend term; then, a grey model is used to fit the trend term, and the Fourier series method is used to fit the periodic terms. Finally, the optimization parameters of the model are solved with the objective of obtaining a minimum mean square error. The novel model is applied to research on the loss rate of agricultural droughts in Henan Province. The average absolute error and root mean square error of the empirical analysis are 0.3960 and 0.5086, respectively. The predicted results show that the novel model can effectively fit the loss rate sequence. Compared with other models, the novel model has higher prediction accuracy and is suitable for the prediction of multiperiod sequences.

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