Application of GM (1, 1) Prediction Model in Coal Consumption Market

2013 ◽  
Vol 869-870 ◽  
pp. 554-558
Author(s):  
Hong Ying Lu
Keyword(s):  
Prediction Model ◽  
Coal Mining ◽  
Forecasting Model ◽  
Excellent Performance ◽  
Coal Consumption ◽  
Practical Applications ◽  
Government Department

With the constant development of economy in China, the total coal consumption is gradually increasing. It is a vital task for government department to rationally and scientifically develop coal mining plans. A forecasting model GM (1, 1) is used in prediction of coal consumption-market. The design of this model is analyzed in detail. The performance of this method is evaluated and the result indicates that the model has excellent performance. This method has obtained favorable effects in practical applications.

Development of a direct NGM(1,1) prediction model based on interval grey numbers

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ye Li ◽  
Yuanping Ding ◽  
Yaqian Jing ◽  
Sandang Guo
Keyword(s):  
Genetic Algorithm ◽  
Markov Model ◽  
Prediction Model ◽  
Real Number ◽  
Content Type ◽  
Optimal Weight ◽  
Practical Applications ◽  
Number Sequences ◽  
Integral Development ◽  
Grey Number

PurposeThe purpose of this paper is to construct an interval grey number NGM(1,1) direct prediction model (abbreviated as IGNGM(1,1)), which need not transform interval grey numbers sequences into real number sequences, and the Markov model is used to optimize residual sequences of IGNGM(1,1) model.Design/methodology/approachA definition equation of IGNGM(1,1) model is proposed in this paper, and its time response function is solved by recursive iteration method. Next, the optimal weight of development coefficients of two boundaries is obtained by genetic algorithm, which is designed by minimizing the average relative error based on time weighted. In addition to that, the Markov model is used to modify residual sequences.FindingsThe interval grey numbers’ sequences can be predicted directly by IGNGM(1,1) model and its residual sequences can be amended by Markov model. A case study shows that the proposed model has higher accuracy in prediction.Practical implicationsUncertainty and volatility information is widespread in practical applications, and the information can be characterized by interval grey numbers. In this paper, an interval grey numbers direct prediction model is proposed, which provides a method for predicting the uncertainty information in the real world.Originality/valueThe main contribution of this paper is to propose an IGNGM(1,1) model which can realize interval grey numbers prediction without transforming them into real number and solve the optimal weight of integral development coefficient by genetic algorithm so as to avoid the distortion of prediction results. Moreover, the Markov model is used to modify residual sequences to further improve the modeling accuracy.

Application of the Regularization Chaos Prediction Model in Aero-Engine Performance Parameters

2012 ◽  
Vol 424-425 ◽  
pp. 347-351 ◽  
Author(s):  
Yong Sheng Shi ◽  
Jun Jie Yue ◽  
Yun Xue Song
Keyword(s):  
Prediction Model ◽  
Simulation Analysis ◽  
Search Algorithm ◽  
Engine Performance ◽  
Forecasting Model ◽  
Exhaust Gas ◽  
Gas Temperature ◽  
Engine Exhaust ◽  
Aero Engine ◽  
Exhaust Gas Temperature

Based on the research of complexity and non-linearity of aero-engine exhaust gas temperature (EGT) system, a regularization chaotic prediction model was proposed to build short time forecasting model of EGT. In this paper, in order to gain the best parameter to improve the accuracy of the forecasting model, a simple search algorithm arithmetic was adopted. The simulation analysis shows that the proposed forecasting model obviously exceeded the traditional chaotic forecasting model on prediction accuracy. Therefore, this arithmetic is efficient and feasible for a short-term prediction of aero-engine exhaust gas temperature

Indonesia’s coal and energy plans face hurdles

2017 ◽  
Keyword(s):  
Coal Mining ◽  
Policy Changes ◽  
Coal Consumption ◽  
Mining Sector ◽  
Content Type ◽  
Strong Force ◽  
Energy Demands ◽  
Official Policy ◽  
Indonesian Coal ◽  
Population And Environment

Significance His remarks follow an Indonesian Coal Mining Association forecast in November that coal consumption in 2017 had increased 16% over 2016's levels. The rise in domestic coal consumption is emblematic of Indonesia’s energy demands, and the effects of uncertain overseas demand for Indonesian coal. Impacts The coal lobby will be a strong force in Indonesian politics for decades. Coal-centred energy plans could harm Indonesia’s population and environment. Indonesian renewable energies will be underdeveloped unless official policy changes. Government efforts to draw more income from the mining sector will intensify.

Application of Thick Coal Seam Mining Method Prediction Model Based on Artificial Neural Network

2014 ◽  
Vol 962-965 ◽  
pp. 242-246
Author(s):  
Wen Yu Lv ◽  
Zhi Hui Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coal Seam ◽  
Prediction Model ◽  
Coal Mining ◽  
Mining Method ◽  
Thick Coal Seam ◽  
Method Selection ◽  
Mining Method Selection ◽  
Seam Mining

Because of thick coal seam mining method selection is not only affected by coal seam geological conditions, but also limited by workers, and not fully utilization of experts` experience, the effect of tradition coal mining method selection methods are not ideal. The thick coal seam mining method prediction model based on artificial neural network (TCSMMPM-ANN) was established through the analysis of thick coal seam mining by using Levenberg – Marquardt (L-M) improved algorithm to train network, the simulation results of network test show that this model can provide a new research idea for thick coal seam mining method optimal selection and face economic and technical index prediction, it will have a broad prospect in thick coal mining.

scholarly journals Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Cai-Xia Lv ◽  
Shu-Yi An ◽  
Bao-Jun Qiao ◽  
Wei Wu
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Arima Model ◽  
Mainland China ◽  
Hemorrhagic Fever ◽  
Prediction Performance ◽  
Forecasting Model ◽  
Incidence Data ◽  
One Step ◽  
The One

Abstract Background Hemorrhagic fever with renal syndrome (HFRS) is still attracting public attention because of its outbreak in various cities in China. Predicting future outbreaks or epidemics disease based on past incidence data can help health departments take targeted measures to prevent diseases in advance. In this study, we propose a multistep prediction strategy based on extreme gradient boosting (XGBoost) for HFRS as an extension of the one-step prediction model. Moreover, the fitting and prediction accuracy of the XGBoost model will be compared with the autoregressive integrated moving average (ARIMA) model by different evaluation indicators. Methods We collected HFRS incidence data from 2004 to 2018 of mainland China. The data from 2004 to 2017 were divided into training sets to establish the seasonal ARIMA model and XGBoost model, while the 2018 data were used to test the prediction performance. In the multistep XGBoost forecasting model, one-hot encoding was used to handle seasonal features. Furthermore, a series of evaluation indices were performed to evaluate the accuracy of the multistep forecast XGBoost model. Results There were 200,237 HFRS cases in China from 2004 to 2018. A long-term downward trend and bimodal seasonality were identified in the original time series. According to the minimum corrected akaike information criterion (CAIC) value, the optimal ARIMA (3, 1, 0) × (1, 1, 0)12 model is selected. The index ME, RMSE, MAE, MPE, MAPE, and MASE indices of the XGBoost model were higher than those of the ARIMA model in the fitting part, whereas the RMSE of the XGBoost model was lower. The prediction performance evaluation indicators (MAE, MPE, MAPE, RMSE and MASE) of the one-step prediction and multistep prediction XGBoost model were all notably lower than those of the ARIMA model. Conclusions The multistep XGBoost prediction model showed a much better prediction accuracy and model stability than the multistep ARIMA prediction model. The XGBoost model performed better in predicting complicated and nonlinear data like HFRS. Additionally, Multistep prediction models are more practical than one-step prediction models in forecasting infectious diseases.

scholarly journals Organizational and methodological support for Ukrainian coal enterprises marketing activity improvement

2020 ◽  
Vol 168 ◽  
pp. 00031 ◽  
Author(s):  
Aleksy Kwilinski ◽  
Yuliya Zaloznova ◽  
Nataliia Trushkina ◽  
Natalia Rynkevych
Keyword(s):  
Organizational Culture ◽  
Coal Mining ◽  
Policy Development ◽  
Methodological Approach ◽  
Marketing Activity ◽  
Coal Consumption ◽  
Culture Transformation ◽  
Hierarchy Analysis ◽  
Coal Market ◽  
Culture Development

The tendencies of marketing policy development in some world’s mining countries in the light of global transformational changes are considered. The factors influencing the marketing activity of Ukrainian coal mining enterprises are identified. The volumes and structure of coal consumption in Ukraine are analyzed with a purpose to identify trends of demand volatility and specifics of logistics services for different consumer categories. It is substantiated the expediency of marketing networks formation as a form of partnership relations between different counterparties in the coal market. It is proposed the mechanism of realization of public-private partnership in the management of coalmining enterprises marketing activity based on syndicate form of incorporation. It is improved the methodological approach, which by use of the hierarchy analysis method allows selecting the optimal direction of enterprises organizational culture transformation based on defining 12 the most important criteria and their systematization into 4 groups. An integrated assessment of the level of organizational culture development of coal mining enterprises is made using economic and mathematical tools.

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