A novel multivariable grey prediction model and its application in forecasting coal consumption

Duan, Huiming; Luo, Xilin

doi:10.1016/j.isatra.2021.03.024

Cited by 62 publications

(22 citation statements)

References 29 publications

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“…Because of its simple structure, easy to learn and use, suitable for small samples, the grey prediction model has become a research hotspot in recent years. It has been widely used in various fields, for example, ecological environment [12] , [13] , traffic flow prediction [14] , [15] , energy consumption prediction [16] , [17] , [18] , etc. In order to improve the performance of the multi-variable grey prediction model, scholars have done in-depth research on GM(1,N) model from the modeling mechanism, model structure, parameter optimization, and other aspects and obtained many achievements.…”

Section: Literature Reviewmentioning

confidence: 99%

“… Description Ref Methods Application [3] World Health Organization: COVID-19 weekly epidemiological update. Machine learning model [2] Machine learning India [3] RNN, LSTM India [8] CNN Oil Time sequence model [4] ARIMA, Discrete wavelet decomposition France, Italy, Spain, UK, USA [5] TP-SMN-AR Global [9] ARIMA Pakistan Comprehensive model [6] A dynamic SEIR model, AI China [7] Gompertz and Logistic model, ANN Mexico [10] Fuzzy clustering, Neural network Mexico [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , ...…”

Section: Literature Reviewmentioning

confidence: 99%

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A novel grey model based on Susceptible Infected Recovered Model: A case study of COVD-19

Duan

Nie

2022

Physica A: Statistical Mechanics and its Applications

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A novel grey model based on Susceptible Infected Recovered Model: A case study of COVD-19

Duan

Nie

2022

Physica A: Statistical Mechanics and its Applications

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“…e grey prediction model is one of the core parts of the grey system, which is characterized by a few data modelling and simple modelling. At present, it is widely used in energy [2][3][4][5], transportation [6][7][8][9], environment [10,11], and other industries [12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Duan et al [30] proposed a new model based on the property of data accumulation and fractional-order accumulation and used particle swarm optimization algorithm to seek the optimal fraction and then predicted the crude oil consumption in China from 2015 to 2020 by the model. In addition, the authors in [2][3][4][5][31][32][33] have effectively predicted various energy consumption methods, and the grey model has achieved good results in the field of energy.…”

Section: Introductionmentioning

confidence: 99%

An Optimization Grey Bernoulli Model and Its Application in Forecasting Oil Consumption

Pang

Duan

2021

Mathematical Problems in Engineering

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Energy consumption in the world is mainly dependent on fossil energy, and oil is one of the main energy sources. Accurate prediction of oil consumption can provide an important basis for national energy security, which can provide reference and early warning for the implementation of the environmental strategy developed by the government. According to the nonlinearity of the energy system, this paper uses the principle of the grey nonlinear prediction model NGBM(1,1) to improve the background value of the model, and by the simulated annealing algorithm, we put forward the optimized grey nonlinear model ONGBM(1,1). At the same time, the model is applied to the oil consumption of China, Chile, Mexico, and Japan. Based on the validity analysis of the existing data of the four countries, the model ONGBM(1,1) is basically superior to the other six grey forecast models. Finally, ONGBM(1,1) is used to predict the oil consumption of the four countries in the next five years, which can provide effective information for energy economic policy.

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“…How to select the appropriate model structure has gradually become an important topic according to the data characteristics. To determine the optimal factors that affect the system development trend, Duan et al (2021) and Cao et al (2021) analyzed the correlation degree between influencing factor sequence and the main feature sequence by the grey comprehensive correlation model; Zeng et al (2019) investigated a new multivariable grey prediction model with structure compatibility, but did not discuss the method of model selection; Xie et al (2021) employed least shrinkage and selection operator (LASSO) to implement the variable selection in the robust reweighted multivariate grey model; Luo et al (2020a, b) discussed the influence of variable selection on model performance, and proposed a multi-criteria variable selection method based on subset selection.…”

Section: Introductionmentioning

confidence: 99%

Data-driven structure selection for the grey NGMC(1,N) model

Luo

Sun

2021

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PurposeWith the prosperity of grey extension models, the form and structure of grey forecasting models tend to be complicated. How to select the appropriate model structure according to the data characteristics has become an important topic. The purpose of this paper is to design a structure selection method for the grey multivariate model.Design/methodology/approachThe linear correction term is introduced into the grey model, then the nonhomogeneous grey multivariable model with convolution integral [NGMC(1,N)] is proposed. Then, by incorporating the least absolute shrinkage and selection operator (LASSO), the model parameters are compressed and estimated based on the least angle regression (LARS) algorithm.FindingsBy adjusting the values of the parameters, the NGMC(1,N) model can derive various structures of grey models, which shows the structural adaptability of the NGMC(1,N) model. Based on the geometric interpretation of the LASSO method, the structure selection of the grey model can be transformed into sparse parameter estimation, and the structure selection can be realized by LASSO estimation.Practical implicationsThis paper not only provides an effective method to identify the key factors of the agricultural drought vulnerability, but also presents a practical model to predict the agricultural drought vulnerability.Originality/valueBased on the LASSO method, a structure selection algorithm for the NGMC(1,N) model is designed, and the structure selection method is applied to the vulnerability prediction of agricultural drought in Puyang City, Henan Province.

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A novel multivariable grey prediction model and its application in forecasting coal consumption

Cited by 62 publications

References 29 publications

A novel grey model based on Susceptible Infected Recovered Model: A case study of COVD-19

A novel grey model based on Susceptible Infected Recovered Model: A case study of COVD-19

An Optimization Grey Bernoulli Model and Its Application in Forecasting Oil Consumption

Data-driven structure selection for the grey NGMC(1,N) model

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