Forecasting of Roof Temperature in a Grey Prediction Model with Optimal Fractional Order Accumulating Operator

2018 ◽  
pp. 500-512
Author(s):  
Yuan Zhang ◽  
Xiaoyong Peng ◽  
Wei Hu
Keyword(s):  
Prediction Model ◽  
Fractional Order ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Roof Temperature

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.

scholarly journals A Novel Fractional-Order Grey Prediction Model and Its Modeling Error Analysis

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 167 ◽  
Author(s):  
Wei Meng ◽  
Bo Zeng ◽  
Shuliang Li
Keyword(s):  
Numerical Simulation ◽  
Prediction Model ◽  
Fractional Order ◽  
Simulation Method ◽  
Percentage Error ◽  
Grey Prediction ◽  
Modeling Error ◽  
Grey Prediction Model ◽  
Special Cases ◽  
The Mean

Based on the grey prediction model GM(1,1), a novel fractional-order grey prediction model is proposed and its modeling error is systematically studied. In this paper, exponential data sequences are generated for numerical simulation. Via the numerical simulation method, the mean absolute percentage error (MAPE) of the fractional-order GM(1,1) with different values of order and development coefficient is compared to the GM(1,1) and the discrete GM(1,1). The error distribution of the sequences of exponential data is given. The GM(1,1) and the direct modeling GM(1,1) are both special cases of the fractional-order GM(1,1). The conclusion is helpful to further optimize the grey model using fractional-order operators and to expand the applicable bound of GM(1,1).

Dynamic Analysis and Prediction of Ecological Footprint in Jilin Province of China Based on Grey Prediction Model

2014 ◽  
Vol 472 ◽  
pp. 899-903 ◽  
Author(s):  
Biao Gao ◽  
Qing Tao Xu
Keyword(s):  
Prediction Model ◽  
Ecological Footprint ◽  
Jilin Province ◽  
Grey Prediction ◽  
Dynamic Changes ◽  
Ecological Capacity ◽  
Grey Prediction Model ◽  
Ecological Deficit ◽  
Gray Prediction ◽  
Per Capita

The paper calculates ecological footprint per capita and ecological capacity per capita in the Jilin province during 1998 and 2010 by using the ecological footprint theory, and analyzes the dynamic changes of ecological footprint per capita and ecological capacity per capita, and obtains development prediction model of ecological footprint per capita and ecological capacity per capita based on grey prediction model. The results indicate the ecological footprint per capita had increased continuously from 1.7841 hm2 per capita to 3.2013 hm2 per capita between 1998 and 2010. During this period, ecological capacity per capita dropped from 1.3535 hm2 per capita to 1.3028 hm2 per capita. Ecological deficit had increased from 0.4306 hm2 per capita to 1.8985 hm2 per capita that showed that the development of Jilin province was in an unsustainable status. The gray prediction model shows the ecological footprint per capita in the Jilin province will increase from 3.4833 hm2 per capita to 5.7022 hm2 per capita between 2011 and 2020, ecological capacity per capita will drop from 1.2978 hm2 per capita to 1.2676 hm2 per capita and ecological deficit will increase from 2.1855 hm2 per capita to 4.4346 hm2 per capita.

scholarly journals Forecasting the Number of New Coronavirus Infections Us-ing an Improved Grey Prediction Model

2021 ◽  
Author(s):  
Hui Li ◽  
Bo Zeng ◽  
Jianzhou Wang ◽  
Hua’an Wu
Keyword(s):  
Prediction Model ◽  
Particle Swarm Optimization Algorithm ◽  
Percentage Error ◽  
Small Data ◽  
Grey Prediction ◽  
Matlab Simulation ◽  
Short Term ◽  
New Model ◽  
Grey Prediction Model ◽  
Coronavirus Infections

Background: Recently, a new coronavirus has been rapidly spreading from Wuhan, China. Forecasting the number of infections scientifically and effectively is of great significance to the allocation of medical resources and the improvement of rescue efficiency. Methods: The number of new coronavirus infections was characterized by “small data, poor information” in the short term. The grey prediction model provides an effective method to study the prediction problem of “small data, poor information”. Based on the order optimization of NHGM(1,1,k), this paper uses particle swarm optimization algorithm to optimize the background value, and obtains a new improved grey prediction model called GM(1,1|r,c,u). Results: Through MATLAB simulation, the comprehensive percentage error of GM(1,1|r,c,u), NHGM(1,1,k), UGM(1,1), DGM(1,1) are 2.4440%, 11.7372%, 11.6882% and 59.9265% respectively, so the new model has the best prediction performance. The new coronavirus infections was predicted by the new model. Conclusion: The number of new coronavirus infections in China increased continuously in the next two weeks, and the final infections was nearly 100 thousand. Based on the prediction results, this paper puts for-ward specific suggestions.

Research on Method of Metro Monitoring Model Based on Grey Prediction Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 641-645
Author(s):  
Mao Hua Liu ◽  
Xiu Bo Sun
Keyword(s):  
Prediction Model ◽  
Safety Monitoring ◽  
Prediction Algorithm ◽  
Grey Prediction ◽  
Monitoring Point ◽  
Value Analysis ◽  
Grey Prediction Model ◽  
Monitoring Model ◽  
Surface Monitoring ◽  
Set Up

Grey prediction model is a model to predict the trend maturely, its application in the subway safety monitoring is of great significance. Set up by MATLAB software to complete the grey prediction model, and take the surface monitoring point for example, Comparing the prediction value with the actual measured value, analysis by the accuracy, obtain the trend of surface change around the subway station.

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
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