scholarly journals Research on Prediction Accuracy of Coal Mine Gas Emission Based on Grey Prediction Model

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1147
Author(s):  
Jun Zeng ◽  
Qinsheng Li
Keyword(s):  
Prediction Model ◽  
Coal Mine ◽  
Prediction Error ◽  
Prediction Accuracy ◽  
Coal Mines ◽  
Grey Prediction ◽  
Gas Emission ◽  
Grey Prediction Model ◽  
Gray Prediction ◽  
Working Face

In order to achieve the accuracy of gas emission prediction for different workplaces in coal mines, three coal mining workings and four intake and return air roadway of working face in Nantun coal mine were selected for the study. A prediction model of gas emission volume based on the grey prediction model GM (1,1) was established. By comparing the predicted and actual values of gas emission rate at different working face locations, the prediction error of the gray prediction model was calculated, and the applicability and accuracy of the gray prediction method in the prediction of gas gushing out from working faces in coal mines were determined. The results show that the maximum error between the predicted and actual measured values of the gray model is 2.41%, and the minimum value is only 0.07%. There is no significant prediction error over a larger time scale; the overall prediction accuracy is high. It achieves the purpose of accurately predicting the amount of gas gushing from the working face within a short period of time. Consequently, the grey prediction model is of great significance in ensuring the safety production of coal mine working face and promote the safety management of coal mine.

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 The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tongfei Lao ◽  
Xiaoting Chen ◽  
Jianian Zhu
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Small Sample ◽  
Grey Prediction ◽  
Dynamic Background ◽  
Background Values ◽  
Grey Prediction Model ◽  
Model Based

As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM (1, N) model is the most popular and important grey model, in which the first “1” stands for the “first order” and the second “N” represents the “multivariate.” The construction of the background values is not only an important step in grey modeling but also the key factor that affects the prediction accuracy of the grey prediction models. In order to further improve the prediction accuracy of the multivariate grey prediction models, this paper establishes a novel multivariate grey prediction model based on dynamic background values (abbreviated as DBGM (1, N) model) and uses the whale optimization algorithm to solve the optimal parameters of the model. The DBGM (1, N) model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy. It is a grey prediction model with extremely strong adaptability. Finally, four cases are used to verify the feasibility and effectiveness of the model. The results show that the proposed model significantly outperforms the other 2 multivariate grey prediction models.

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.

Non-equidistant non-homogenous grey prediction model with fractional accumulation and its application

2021 ◽  
pp. 1-14
Author(s):  
Jia-Nian Zhu ◽  
Xu-Chong Liu ◽  
Chong Liu
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Model Based ◽  
Whale Optimization ◽  
High Prediction ◽  
Best Parameters ◽  
Better Than

Non-equidistant non-homogenous grey model (abbreviated as NENGM (1,1, k) model) is a grey prediction model suitable for predicting time series with non-equal intervals. It is widely used in various fields of society due to its high prediction accuracy and strong adaptability. In order to further improve the prediction accuracy of the NENGM (1,1, k) model, the NENGM (1,1, k) model is optimized in terms of the cumulative order and background value of the NENGM (1,1, k) model, and a NENGM (1,1, k) model based on double optimization is established (abbreviated as FBNENGM (1,1, k) model), and the whale optimization algorithm is used to solve the best parameters of the model. In order to verify the feasibility and validity of the FBNENGM (1,1, k) model, the FBNENGM (1,1, k) model and other four prediction models are applied to three cases respectively, and three indexes commonly used to evaluate the performance of prediction models are used to distinguish. The results show that the prediction accuracy of the FBNENGM (1,1, k) model based on double optimization is better than other prediction models.

Application of Improved Grey Prediction Model in Jilin Province of the Software Industry

2014 ◽  
Vol 998-999 ◽  
pp. 1079-1082 ◽  
Author(s):  
Wei Shi Yin ◽  
Pin Chao Meng ◽  
Yan Zhong Li
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Historical Data ◽  
Software Industry ◽  
Experimental Results ◽  
Jilin Province ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Better Than

Based on the modified grey prediction model, the outputs of software industry in Jilin Province were predicted. First the historical data and updated the data were pre-treated by iteration. Then it was found that the results from the modified grey prediction model were better than that from traditional grey prediction model by residual analysis. Finally, the prediction about the outputs of software industry in Jilin Province was given for the next five years. According to the experimental results, our proposed new method obviously can improve the prediction accuracy of the original grey model.

Application of Multivariable Grey Prediction

2010 ◽  
Vol 44-47 ◽  
pp. 2717-2723
Author(s):  
Ling Ling Li ◽  
Jun Jie Han ◽  
Meng Wu ◽  
Zhi Gang Li
Keyword(s):  
Prediction Model ◽  
Multivariable Analysis ◽  
Single Variable ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Gray Prediction ◽  
Relevant Variables ◽  
Multiple Variables ◽  
System Point

In this paper, a multivariable analysis of grey prediction model MGM (1,n) is proposed ,which is based on single-variable gray prediction model GM (1,1). For multiple variables of a system, they influence each other and interrelate, MGM (1,n) model is description of each of the major relevant variables in the system from system point of view.Through comparison of an example of forecasting relay failure, can be obtained: multivariable grey prediction is more accurate and more close to the actual value than the single variable prediction.

Coal Mine Gas Prediction Model Based on Ant Colony Neural Network

2012 ◽  
Vol 546-547 ◽  
pp. 3-7 ◽  
Author(s):  
Jia Tang Cheng ◽  
Hui Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Coal Mine ◽  
Prediction Accuracy ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Gas Emission ◽  
Bp Network ◽  
Coal Mine Gas ◽  
Mine Gas

In order to improve the prediction accuracy and prediction speed of coal mine gas emission, ant colony algorithm combining with neural network is used for prediction models design. Choose an important factor influencing gas emission, establish of its neural network prediction model. Select the network mean square error as the objective function, through the ant colony algorithm iteration achieve optimal BP network weights, and use the optimized BP network for gas emission prediction. Simulation results show that the method has high fitting prediction accuracy.

scholarly journals Forecasting Oil Consumption with Novel Fractional Grey Prediction Model Based on Simpson Formula

2019 ◽  
pp. 1-27
Author(s):  
Xiwang Xiang ◽  
Peng Zhang ◽  
Lang Yu
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Model Error ◽  
Grey System Theory ◽  
Human Society ◽  
Grey Prediction ◽  
Oil Consumption ◽  
Grey System ◽  
Grey Prediction Model ◽  
Grey Models

With the development of human society, the evolving transition of energy will become a common challenge that mankind has to face together. In this context, it is crucial to make scientific and reasonable predictions about energy consumption. This paper presents a novel fractional grey prediction model FGM(1,1,k2) based on the classical fractional grey system theory. In order to improve the prediction accuracy of the FGM(1,1,k2) model, we further analyze the model error and propose improved grey model called as SFGM with optimization of background value. The numerical cases point out that SFGM(1,1,k2) significantly outperforms other existing fractional grey models. Finally, the proposed SFGM(1,1,k2) is applied to the forecasting of oil consumption, the predicted results would provide a reference for making energy policy in new situations.

scholarly journals Prediction and analysis of domestic water consumption based on optimized grey and Markov model

2021 ◽  
Author(s):  
Zhaocai Wang ◽  
Xian Wu ◽  
Huifang Wang ◽  
Tunhua Wu
Keyword(s):  
Markov Model ◽  
Prediction Model ◽  
Water Consumption ◽  
Prediction Accuracy ◽  
Rapid Development ◽  
Shaanxi Province ◽  
Grey Prediction ◽  
Domestic Water ◽  
Grey Prediction Model ◽  
Domestic Water Consumption

Abstract With the rapid development of urbanization and the continuous improvement of living standards, China's domestic water consumption shows a growing trend. However, in some arid and water deficient areas, the shortage of water resources is a crucial factor affecting regional economic development and population growth. Therefore, it is essential important to reliably predict the future water consumption data of a region. Aiming at the problems of poor prediction accuracy and overfitting of non-growth series in traditional grey prediction, this paper uses residual grey model combined with Markov chain correction to predict domestic water consumption. Based on the traditional grey theory prediction, the residual grey prediction model is established. Combined with the Markov state transition matrix, the grey prediction value is modified, and the model is applied to the prediction of domestic water consumption in Shaanxi Province from 2003 to 2019. The fitting results show that the accuracy grade of the improved residual grey prediction model is “good”. This shows that the dynamic unbiased grey Markov model can eliminate the inherent error of the traditional grey GM (1,1) model, improve the prediction accuracy, have better reliability, and can provide a new method for water consumption prediction.

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