DGM Model Research Articles

Forecasting the annual sales of new energy vehicles in China based on the DGM (1, N) model

New energy vehicles are of great significance to China’s economy and ecological environment. Selected from 2012 to 2022 new energy vehicles annual sales and 19 related factors data, gray correlation analysis, select the gray correlation of large charging pile ownership, new energy vehicles related enterprise registration, power battery loading, a total of three factors as a variable DGM (1,3) model, calculation parameters, and the validity of the model, the results proved that the model prediction credibility and prediction accuracy is high, the relative error, reasonable expectations for the future policy, industrial planning, investment strategy, etc.

Fault prediction of pneumatic valves in an LNG plant by the DGM(1, 1) model

Fault prediction of pneumatic valves in an LNG plant by the DGM(1, 1) model

Influence of desorption hysteresis effects on coalbed methane migration and production based on dual-porosity medium model incorporating hysteresis pressure

Desorption hysteresis affects gas mass transfer and migration in coal seams, which is not considered in current dual-porosity medium models, leading to inaccurate assessment and prediction of gas extraction. This study proposed a desorption hysteresis pressure to address this issue of the gas mass transfer in the model. On this basis, two coupled gas migration models, considering (DGMH) and without considering (DGM) desorption hysteresis effects, were developed incorporating mass transfer, gas desorption, gas seepage, and permeability evolution. The models were solved and compared using the finite element method. The results demonstrate that the DGM model yields higher production assessment compared to the DGMH model. This discrepancy can be attributed to the hysteresis effects, which prevent the matrix gas from supplementing the fractures timely after fracture gas flows into the borehole. The DGMH model shows less mass transfer, coal seam permeability, and gas extraction compared to the DGM model. The desorption hysteresis effects are more pronounced in coal seams with higher permeability. In addition, neglecting hysteresis effects when calculating permeability from field gas production data may result in inaccurate values. The hysteresis effects is recommended to be considered in the prediction of coalbed methane extraction, permeability calculation, and risk control of delayed coal and gas outbursts.

A Novel DGM(1, N) Model with Interval Grey Action Quantity and Its Application for Forecasting Hydroelectricity Consumption of China

This paper addresses the issue of the conventional DGM(1, N) model’s prediction results not taking into account the grey system theory pri1nciple of the “non-uniqueness of solutions”. Firstly, before presenting the interval grey action quantity, the practical significance of grey action quantity is examined. In the DGM(1, N) model, the grey action quantity is transformed into an interval grey action quantity. Then, the calculation of the parameters uses the least squares method. A DGM(1, N, ⊗c) model containing interval grey action is then built, and meanwhile, the program code for DGM(1, N, ⊗c) is provided. Lastly, the aforementioned model is used to forecast the hydroelectricity consumption of China. The findings indicate that it produces more rational outcomes than the traditional DGM(1, N) model. Overall, the research carries significant pragmatic implications for broadening the conceptual underpinnings of multivariate grey forecasting models and enhancing their structural arrangement.

Forecasting per Capita Energy Consumption in China Using a Spatial Discrete Grey Prediction Model

To overcome the limitations of the present grey models in spatial data analysis, a spatial weight matrix is incorporated into the grey discrete model to create the SDGM(1,1,m) model, and the L1-SDGM(1,1,m) model is proposed, considering the time lag effect to realize the simultaneous forecasting of spatial data. The validation of the SDGM(1,1,m) and L1-SDGM(1,1,m) models is achieved, and finally, the per capita energy consumption levels (PCECs) of 30 provinces in China from 2020 to 2025 is predicted using SDGM(1,1,m) with a metabolic mechanism. We draw the following conclusions. First, the SDGM(1,1,m) and L1-SDGM(1,1,m) models established in this paper are reasonable and improve forecasting accuracy while supporting interactive regional forecasting. Second, although SDGM(1,1,m) resembles the DGM(1,n) model, their modeling conditions and targets are different. Third, the SDGM(1,1,m) and L1-SDGM(1,1,m) models can be used to effectively analyze the spatial spillover effects within the selected modeling interval while achieving accurate predictions; notably, from 2010 to 2017, the PCECs of Inner Mongolia and Qinghai were most affected by spatial factors, while the PCECs of Jilin, Jiangxi, and other provinces were influenced little by spatial factors. Fourth, predictions indicate that the PCECs of most Chinese provinces will increase under the current grey conditions, while the PCECs of provinces such as Beijing are expected to decrease.

Deformation Prediction of Dam Based on Optimized Grey Verhulst Model

Dam deformation monitoring data are generally characterized by non-smooth and no-saturated S-type fluctuation. The grey Verhulst model can get better results only when the data series is non-monotonic swing development and the saturated S-shaped sequence. Due to the limitations of the grey Verhulst model, the prediction accuracy will be limited to a certain extent. Aiming at the shortages in the prediction based on the traditional Verhulst model, the optimized grey Verhulst model is proposed to improve the prediction accuracy of the dam deformation monitoring. Compared with those of the traditional GM (1,1) model, the DGM (2,1) model, and the traditional Verhulst (1,1) model, the experimental results show that the new proposed optimized Verhulst model has higher prediction accuracy than the traditional gray model. This study offers an effective model for dealing with the non-saturated fluctuation sequence to predict dam deformation under uncertain conditions.

A novel discrete GM(2,1) model with a polynomial term for forecasting electricity consumption

• A novel discrete GM(2,1) model with a polynomial term is proposed. • The generalization and adaptability of the proposed model were validated from the theoretical and practical perspectives. • The newly-designed model is applied to predict per capita living electricity consumption. The forecast of electrical energy demand has played an increasingly relevant role in sustainable electrical power system. This paper develops a new method for forecasting China's per capita living electricity consumption by grey modelling technique. Considering the multiple and mixed change patterns, a novel discrete grey model with polynomial term (abbreviated as DGM(2,1, k n )) is proposed in this study. Firstly, the polynomial term is introduced into the discrete DGM(2,1) model. Secondly, the Tikhonov regularization method is employed to solve the overfitting problem. Lastly, two published cases and China's per capita living electricity consumption are used to validate the generalization and adaptability of the newly-designed model. The numerical results of such experiments show that the proposed model outperforms other competitive models in terms of accuracy level. Therefore, the projections of China's per capita living electricity consumption in 2020 and 2025 have been made for providing a solid reference for the formulation of electrical power strategies.

A moving average denoise operator and grey discrete production process quality prediction model

PurposeThe purpose of this paper is to solve the problem of quality prediction in the equipment production process and provide a method to deal with abnormal data and solve the problem of data fluctuation.Design/methodology/approachThe analytic hierarchy process-process failure mode and effect analysis (AHP-PFMEA) structure tree is established based on the analytic hierarchy process (AHP) and process failure mode and effect analysis (PFMEA). Through the failure mode analysis table of the production process, the weight of the failure process and stations is determined, and the ranking of risk failure stations is obtained so as to find out the serious failure process and stations. The spectrum analysis method is used to identify the fault data and judge the “abnormal” value in the fault data. Based on the analysis of the impact, an “offset operator” is designed to eliminate the impact. A new moving average denoise operator is constructed to eliminate the “noise” in the original random fluctuation data. Then, DGM (1,1) model is constructed to predict the production process quality.FindingsIt is discovered the “offset operator” can eliminate the impact of specific shocks effectively, moving average denoise operator can eliminate the “noise” in the original random fluctuation data and the practical application of the shown model is very effective for quality predicting in the equipment production process.Practical implicationsThe proposed approach can help provide a good guidance and reference for enterprises to strengthen onsite equipment management and product quality management. The application on a real-world case showed that the DGM (1,1) grey discrete model is very effective for quality predicting in the equipment production process.Originality/valueThe offset operators, including an offset operator for a multiplicative effect and an offset operator for an additive effect, are proposed to eliminate the impact of specific shocks, and a new moving average denoise operator is constructed to eliminate the “noise” in the original random fluctuation data. Both the concepts of offset operator and denoise operator with their calculation formulas were first proposed in this paper.

Forecasting and Analysis of Energy Consumption in China

Energy is essential to the development of an economy and society. In recent years, China's rapid economic development has created the "China Miracle", but it has also led to a sharp increase in energy consumption in China. To ensure the achievement of the ambitious goal of reaching the carbon peak by 2030, it is of great significance to study the total energy consumption in China in order to promote the national energy conservation and emission reduction actions. This paper constructs models GM(1,1), DGM(1,1), and gray Verhulst model based on the original data of China's total energy consumption from 2001 to 2020, and constructs a combined forecasting model by the least squares method to make an economic forecast of China's energy consumption in the next five years. It provides a theoretical basis for making a reasonable energy planning.

Forecasting the Coffee Consumption Demand in Vietnam Based on Grey Forecasting Model

Forecasting the domestic coffee consumption demand is important for policy planning and making the right decisions. Thus, in this study, we try to find out the most suitable model among three proposed models (GM (1,1), DGM (1,1) and Grey Verhulst model (GVM)) for predicting the amount of domestic coffee consumption in Vietnam in the future. Yearly data of coffee consumption from 2010–2020 are used in this research. The experimental results indicated that the GM (1,1) is the most accurate model selected in this study with the lowest average value of [Formula: see text]%. So, the GM (1,1) model is strongly suggested in the analysis of coffee consumption demand in Vietnam. Finding the right tool will help managers make right decisions easily for sustainable development of the coffee industry in Vietnam in the future.

A New DGM(1,1) Model with a Grey Parameter and Its Application

In the DGM(1,1) model modeling process, the influencing factors are uncertain. But the solution of DGM(1,1) model with uncertain information is unique, which conflicts with the nonuniqueness principle of solution in grey theory. In view of this situation, this paper makes an in-depth analysis of the meaning of grey action quantity <i>β</i>2 in DGM(1,1) model and regards <i>β</i>2 as an interval grey number. The maximum possibility whitenization value is given to estimate the kernel of grey number, and the typical possibility function is constructed to describe the possibility of grey number taking different values. A new DGM(1,1) model with a grey parameter is then proposed, whose simulation results are interval grey numbers. The proposed model is compatible with the DGM(1,1) model in model structure and simulation results. Finally, the practical example results show the applicability and effectiveness of the proposed model.

A Novel Discrete DGM (2, 1, t2) Model Based on Stochastic Oscillation Sequence and Its Application

This article aims to improve the prediction accuracy of the grey model for stochastic oscillation sequence, to overcome the defect of the direct span from difference to differential and to better depict the growth trend of data series. Firstly, the accelerated translation smoothing transformation is constructed, which transforms the stochastic oscillation sequence into the monotonic increasing sequence that is suitable for modeling. Secondly, the quadratic time power term is introduced to establish a novel discrete DGM (2, 1, t2) model, then derive a novel simulation prediction formula, and analyze properties of this novel model; it was concluded that its application scope is expanded. Finally, a novel discrete DGM (2, 1, t2) model based on a stochastic oscillation sequence is established to forecast a group of stochastic oscillation sequences with different amplitudes and two different city traffic flow series and compare the prediction accuracy with other grey models; the results show that the feasibility of this model is verified and has good simulated prediction effect in the field of traffic flow.

Remaining Useful Life for Aircraft Engine Forecasting Based on Data-driven

As for the low forecasting precision problem about time series in avionics complex system, a time series combined forecasting model of complex systems was established based on GM (grey model is referred to as GM). However, using the traditional GM(1, 1) model to predict complex system failures might result in huge forecasting errors. To solve the drawbacks of the traditional GM(1, 1) model, this work developed the traditional GM(1, 1) model (abbreviated as DGM(1, 1)), which can predict non-geometric sequences more correctly. To reduce such mistakes, a PS-DGM (1, 1) forecast model was created by combining DGM(1, 1) with the particle-swarm optimization process. The DGM(1, 1) model was utilized to generate the forecasting sequence, and then the particle-swarm optimization approach was employed to further alter them, resulting in the degradation trend prediction of avionics complex system characteristic parameters. The experimental results show that the PS-DGM(1, 1) model’s validity was confirmed by tracking and forecasting avionics equipment experiment data in a real-world setting, and that the suggested model achieves higher precision than the old one.

A novel structural adaptive discrete grey prediction model and its application in forecasting renewable energy generation

The rapidly growing renewable energy generation instigates stochastic volatility of electricity supply that may compromise the power grid's stability and increase the grid imbalance cost. Therefore, accurate mid-to-long term renewable energy generation forecasting is of great significance for integrating renewable energy systems with smart grid and energy strategic planning. For this purpose, a new structural adaptive discrete grey prediction model is proposed. Overall, the proposed model possesses three main contributions. Firstly, the introduction of nonlinear term and periodic term strengthens the ability of the traditional DGM (1,1) model to capture the nonlinear and linear development trend of time series and improves the adaptability of the grey prediction model to arbitrary periodic time series. Secondly, the emerging coefficients are determined by the particle swarm optimization algorithm and hold-out cross-validation method, and the adaptive selection of the model structure is realized. From the perspective of expert system, it reduces the need for modeling knowledge. Thirdly, the consistency of stretching, unbiasedness, and compatibility with other grey models are discussed, which further verified the feasibility and practicability of the proposed model. Besides, the performance of the proposed model is compared with those of a series of grey prediction models and non-grey prediction methods to verify the feasibility and superiority of this new approach by three real cases. The results indicate that the proposed model benefits from its adaptive structure and produces reliable predictions.

Forecasting Hydrogen Energy Production in China base on Multi Grey Model

As hydrogen energy becomes an important fuel energy, the demand is also increasing. With the rapid development of hydrogen energy industry in China, the application of hydrogen energy is more and more extensive. In order to predict the hydrogen energy production in China, three grey models, GM(1, 1) model, DGM model and Grey Verhulst model, were established according to the data of hydrogen energy production in China from 2009 to 2019. Through the comparison and analysis of the predicted data, it is concluded that the Verhulst model is more accurate and more suitable for the prediction and analysis of China’s hydrogen energy production. At the same time, the prediction results also show that the hydrogen energy production prediction model established by using grey theory is feasible in practical work. According to the prediction model, China’s hydrogen energy from 2020 to 2021 will be 22.81 million tons and 22.96 million tons respectively.

Application of Grey System Model to Forecast the Natural Gas Imports in China

China’s natural gas imports will keep an upward trend in the future due to its increasing demands. A comparatively accurate prediction of natural gas imports will help the Chinese government make appropriate decisions when formulating energy policies. In this paper, a new grey predication model, GPM_NGI model, was proposed to forecast China’s natural gas imports. Compared with GM (1, 1) and DGM (1, 1) model, the proposed new model performed better in the simulation process and bore smaller mean relative percentage error when used in simulating China’s natural gas imports from 2011 to 2019. Then, the new model was employed to forecast China’s natural gas imports from 2020 to 2022. The results showed that China’s natural gas imports would continue to grow rapidly over the next three years. Therefore, in order to strike a balance between the natural gas supply and demand in the future and avoid overdependence on imports, the Chinese government should take effective measures from both the supply and demand ends, such as making better use of shale gas, wind, and solar energy as well as reducing the industrial dependence on natural gas.

Forecasting China’s per Capita Living Energy Consumption by Employing a Novel DGM (1, 1, tα) Model with Fractional Order Accumulation

The living energy consumption of residents has become an important technical index to promote the economic and social development strategy. The country’s medium- and short-term living energy consumption is featured with both a certainty of annual increment and an uncertainty of random variation. Thus, it can be seen as a typical grey system and shall be suitable for the grey prediction model. In order to explore the future development trend of China’s per capita living energy consumption, this paper establishes a novel grey model based on the discrete grey model with time power term and the fractional accumulation (FDGM (1, 1, tα) for short) for forecasting China’s per capita living energy consumption, which makes the existing model to adapt to different time series by adjusting fractional order accumulation parameter and power term. In order to verify the feasibility and effectiveness of the novel model, the proposed and eight other existing grey prediction models are applied to the case of China’s per capita living energy consumption. The results show that the proposed model is more suitable for predicting China’s per capita energy consumption than the other eight grey prediction models. Finally, the proposed model based on metabolism mechanism is used to predict China’s per capita living energy consumption from 2018 to 2029, which can provide a reference for energy companies or government decision makers.

Predicting of Ship Magnetic Field Underwater Based on Grey Theory Model

Based on the grey theory DGM (1,1) model, the ship magnetic field at different depths is predicted. COMSOL is used to simulate the ship’s magnetic field, and the simulated magnetic field value is applied to the established model. The gray model is used to predict the ship’s underwater magnetic field at different depths, which verifies the effectiveness of the gray prediction model.

FDGM(1,1) model based on unified fractional grey generation operator

PurposeThe purpose of this paper is to unify the expression of fractional grey accumulating generation operator and the reducing generation operator, and build the FDGM(1,1) model with the unified fractional grey generation operator.Design/methodology/approachBy systematically studying the properties of the fractional accumulating operator and the reducing operator, and analyzing the sensitivity of the order value, a unified expression of the fractional operators is given. The FDGM(1,1) model with the unified fractional grey generation operator is established. The relationship between the order value and the modeling error distribution is studied.FindingsThe expression of the fractional accumulating generation operator and the reducing generation operator can be unified to a simple expression. For −1&lt;r &lt; 1, the fractional grey generation operator satisfies the principle of new information priority. The DGM(1,1) model is a special case of the FDGM(1,1) model with r = 1.Research limitations/implicationsThe sensitivity of the unified operator is verified through random numerical simulation method, and the theoretical proof was not yet possible.Practical implicationsThe FDGM(1,1) model has a higher modeling accuracy and modeling adaptability than the DGM(1,1) by optimizing the order.Originality/valueThe expression of the fractional accumulating generation operator and the reducing generation operator is firstly unified. The FDGM(1,1) model with the unified fractional grey generation operator is firstly established. The unification of the fractional accumulating operator and the reducing operator improved the theoretical basis of grey generation operator.

Model comparison of GM(1,1) and DGM(1,1) based on Monte-Carlo simulation

Discrete grey model (DGM(1,1)) is considered to be superior to grey model (GM(1,1)) because it can completely simulate the pure exponential sequences. However, owing to practical data generation process is interfered by random factors, the superiority of DGM(1,1) model to GM(1,1) model cannot be widely and reliably validated in practical applications. Therefore, by utilizing the Monte-Carlo simulation method, groups of completely random sequences conforming to different distributions are randomly generated and the predictive capabilities of the two models are compared. In addition, the novel grey models of fractional order accumulation (FGM(1,1) and FDGM(1,1)) are introduced for further comparison. The results show that the predictive capabilities of the two models for random sequences conforming to normal distribution are nearly equivalent. However, the predictive capabilities of DGM(1,1) model for the other three kinds of random sequences are all superior to those of GM(1,1) model. The parameters change of completely random sequences influences the predictive capabilities of the two models. The parameters change of random sequences with exponential trend can influence the predictive capability of GM(1,1) model while has no significant influence on the predictive capability of DGM(1,1) model.