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.

Study on Power Load Forecasting Base on Grey Prediction Model by Slip Average Method

2011 ◽  
Vol 84-85 ◽  
pp. 752-756
Author(s):  
Zheng Yuan Jia ◽  
Zhi Wei Huang ◽  
Chun Mei Wang ◽  
Gang Zhang
Keyword(s):  
Prediction Model ◽  
Electric Power ◽  
Average Method ◽  
Original Data ◽  
Power Demand ◽  
Grey Prediction ◽  
Factors Affecting ◽  
Grey Prediction Model ◽  
Power Load ◽  
Set Up

The grey control theory is used to predict electric power demand in this paper. Original data is processed by the Generation Method. Many unimportant factors affecting electric power demand are removed,and useful information is extracted from original data. The differential fitting equation is set up,and grey prediction model modified by slip average method is presented with residual modification. The current year data is possessed with high weight,which avoids excessive fluctuation. Predicting results show that the model is effective to improve the predict precision.

Forecasting Mechanism using Grey Prediction Model for Sudden Water Pollution Accidents

2020 ◽  
Vol 50 (2) ◽  
pp. 03-05
Author(s):  
Melville Dupuis
Keyword(s):  
Water Pollution ◽  
Prediction Model ◽  
Water Contamination ◽  
Contextual Analysis ◽  
Grey Prediction ◽  
Crisis Plans ◽  
Grey Prediction Model ◽  
Set Up ◽  
Sudden Water Pollution

To discover the control of water contamination mishaps, measurements and conjecture strategies are frequently used to define crisis plans in security designing. This paper shows the inherent clash between the methods of water-shed the executives and counteraction of abrupt water pollution disaster. Consequently, another idea "Trans-limit abrupt water pollution disaster" has been projected, and attributes gauging instrument is explored. In this magazine, GM (1,1) model is utilized in expectation of water contamination mishaps that occurred as of late. We utilize a showing case of water contamination as our contextual analysis to test the proficiency and precision of the GM(1,1) representation. On the base of model, we pick 9 determining files finally, and set up the estimating file framework for trans-limit abrupt water contamination mishaps. As per the test marks, the presentation of this mould is acceptable and it is helpful for regime to settle on crisis choices.

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.

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 A Hybrid Grey Prediction Model for Small Oscillation Sequence Based on Information Decomposition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Meng Zhou ◽  
Bo Zeng ◽  
Wenhao Zhou
Keyword(s):  
Prediction Model ◽  
Small Oscillation ◽  
New Method ◽  
Superior Performance ◽  
Small Data ◽  
Grey Prediction ◽  
New Model ◽  
Grey Prediction Model ◽  
Proposed Model ◽  
Oscillation Characteristic

Grey prediction model has good performance in solving small data problem, and has been widely used in various research fields. However, when the data show oscillation characteristic, the effect of grey prediction model performs poor. To this end, a new method was proposed to solve the problem of modelling small data oscillation sequence with grey prediction model. Based on the idea of information decomposition, the new method employed grey prediction model to capture the trend characteristic of complex system, and ARMA model was applied to describe the random oscillation characteristic of the system. Crops disaster area in China was selected as a case study and the relevant historical eight-year data published by government department were substituted to the proposed model. The modelling results of the new model were compared with those of other traditional mainstream prediction models. The results showed that the new model had evidently superior performance. It indicated that the proposed model will contribute to solve small oscillation problems and have positive significance for improving the applicability of grey prediction model.

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