Grey Prediction Model Research Articles

An entropy-based TOPSIS and optimized grey prediction model for spatiotemporal analysis in strategic emerging industry

Stepping into the stage of high-quality and innovation-driven development, the 14th Five-Year Plan for China places a high priority on advancing the strategic emerging industry (SEI). An integrated strategic emerging industry-economy-environment (IEE) system is constructed to investigate the sustainability of SEI. The development quality of the three subsystems is assessed by the entropy-based TOPSIS. Furthermore, the evolution trend, spatial distribution, and the prospects of the coupling coordination degree (CCD) for the IEE system are researched. Findings indicate that the respective evolution trend of CCD exhibits an N-shape pattern from 2011 to 2019 in the 11 districts of the Yangtze River Economic Belt. Moreover, mutual promotion and interaction exist between the strategic emerging industrial system and the economic system reflected by analogous evolution patterns. The spatial distribution for CCD is featured with a stepwise decline among these 11 districts from lower to upper reaches of the Yangtze River, mostly attributed to the lagged industrial and economic growth in the districts of upper and middle reaches. As predicted by the improved grey Bernoulli model, the prospect for CCD generally will experience an upward trend. Based on the empirical results, countermeasures are suggested to promote the sustainability and unleash the potential of SEI for advancing high-quality development.

A predictive vampire attack detection by social spider optimized Gaussian mixture model clustering

SummaryA sensor node carries out a specific function in a wireless sensor network. Wireless sensor networks are less bearable but more vital to the industry and the people because of threats. As a result, a node's battery life will be drastically reduced, and the node will be rendered completely inoperable, which is the most extreme form of denial of service attack. A vampire attack, one of the denials of service attacks, may inflict extensive harm to the network, making it harder to detect and using more energy than necessary. This article has proposed a novel vampire attack detection and prevention by the energy consumption prediction in the data path with the least error as low as . In combination with the social spider optimized Gaussian mixture model, the gray prediction model is used to detect and prevent the attack. The energy prediction scheme calculates a cooperative trust score and is categorized by the optimized Gaussian mixture model. The algorithm is validated with recent state‐of‐the‐art schemes and the detection accuracy improvement of up to 35.27% is achieved.

Agricultural carbon emission efficiency evaluation and influencing factors in Zhejiang province, China

Based on the classic IPCC carbon emission calculation theory, this paper calculates the agricultural carbon emissions intensity and efficiency in Zhejiang Province from 2011 to 2020. The LMDI model is further adapted to carry out the influence factors of agricultural carbon emissions. In addition, the grey prediction model GM (1, 1) is used to predict the carbon emissions of Zhejiang Province from 2022 to 2025. The results show that the agricultural carbon emissions and carbon emission intensity in Zhejiang Province have a downward trend. Further, it is concluded that Shaoxing, Hangzhou, Jiaxing, and Huzhou are the cities with low emission and high efficiency, and Wenzhou is the city with high emission and low efficiency. Meanwhile, the improvement of Total Factor Productivity (TFP) results from the joint action of Technical Progress Efficiency (TECH) and Technical Efficiency (EFF). TECH is greater than EFF, and Scale Efficiency (SE) and Pure Technical Efficiency (PTE) contributions change with the years. In general, the contribution of PTE is more significant than that of SE, and its improvement mainly rests on technical progress. Among the factors influencing agricultural carbon emission efficiency, agricultural carbon emission intensity and labor force size have inhibiting effects on agricultural carbon emission efficiency growth. In contrast, agricultural industrial structure, economic development, and urbanization positively affect agricultural carbon emission efficiency. The prediction results show that the overall carbon emissions of Zhejiang Province will get a downward trend. Finally, based on these findings, we offer policy implications.

A Platoon-Based Hierarchical Merging Control for On-Ramp Vehicles Under Connected Environment

Connected autonomous vehicle technology is conductive to promoting the transition from traditional merging control (e.g., ramp metering) to automated merging control. This paper proposes a platoon-based hierarchical merging control algorithm for on-ramp vehicles to achieve automated merging control under connected traffic environment. The proposed algorithm optimizes merging maneuvers of on-ramp vehicles to smooth their merging trajectories without frequent decelerations or stops at the end of the ramp, and to minimize disruption to the mainline traffic in the merging zone. A tactical layer controller is designed to select pre-target merging gaps for on-ramp vehicles, in which the future motion (i.e., acceleration and deceleration) of mainline vehicles is considered through the grey prediction model. An operational layer controller is constructed based on model predictive control to adjust the speed of on-ramp vehicles in advance, and controls on-ramp vehicles to merge into the pre-target merging gaps under state constraints (i.e., safe headway, maximum speed and so on). Through numerical simulation, the effectiveness of the proposed algorithm is validated under different merging scenarios. It is shown that on-ramp vehicles smoothly merge into the mainline within the pre-target merging gap at the same speed as adjacent mainline vehicles. Compared with the baseline merging control algorithm, the proposed algorithm significantly reduces both fuel consumption and travel time of on-ramp vehicles, and improves passenger comfort.

A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecasting

• A novel accumulation generation operator is developed by weighting coefficient. • A novel structure adaptive new information priority grey model is proposed. • The robustness analysis of the new model is established. • Comparative study shows that new model is superior to other various grey models. Renewable energy has made a significant contribution to global power generation. Therefore, accurate mid-to-long term renewable energy generation forecasting is becoming more and more important for integrating renewable energy systems with smart grid and energy strategic planning. For this purpose, a novel structure adaptive new information priority discrete grey prediction model is established, and the disturbance analysis shows that it is suitable for small sample modeling. Firstly, the fractional dynamic weighted coefficient is introduced to define the accumulative generating operator satisfying the new information priority principle, which can realize the effective utilization of system information with insufficient information and accurately extract the development mode of system sequence from sparse samples. In terms of model structure, the nonlinear term and periodic fluctuation term are introduced to simulate the nonlinear and periodic fluctuation trend of renewable energy generation data, which improves the adaptability of the grey prediction model to the nonlinear and fluctuating time series. By designing the comparative experiment of optimization algorithm, Grey Wolf Optimizer (GWO) is selected to optimize the structural parameters of the model, giving the proposed model higher flexibility and stronger adaptability. In order to illustrate the performance of the model, the new model is compared with statistical econometrics technology, artificial neural network (ANN) and various existing grey models in three cases: World biofuel power generation, China's renewable energy power generation and small-scale solar photovoltaic power generation in the United States. The experimental results show that the proposed model is superior to other competitive models in terms of fitting accuracy and prediction accuracy. In addition, Monte-Carlo Simulation and probability density analysis further show that the proposed model can provide the best prediction effect and has high robustness.

A novel optimized grey model and its application in forecasting CO[formula omitted] emissions

Carbon dioxide emissions are the main cause of global warming. At present, how to reduce carbon dioxide emissions while promoting energy savings and emission reduction is a hot research topic. Hence, China’s carbon dioxide emissions must be reasonably and accurately predicted because it is very important for the Chinese government to formulate energy and environmental policies. In this study, the classical optimization theory of the Fibonacci sequence and golden ratio were applied to the grey prediction model of an approximately inhomogeneous exponential series. Then, a new optimization model was established, and the properties of the optimization model were studied. The purpose is to reduce the parameter estimation errors of the model and improve the simulation and prediction accuracy of the model. Next, the novel model was applied to the simulation and prediction of CO2 emissions in China. The experimental results show that the effectiveness of the novel model was much better than that of the other models, which confirms the effectiveness of the new model. Based on this, China’s carbon dioxide emissions were predicted and analysed. The results show that China’s carbon dioxide emissions will still be on the rise over the next five years, and carbon dioxide emissions remain a serious problem.

An optimized nonlinear time-varying grey Bernoulli model and its application in forecasting the stock and sales of electric vehicles

An accurate prediction of electric vehicles stock and sales is a prerequisite for planning industrial policies for renewable sources to be used by a transportation system. We propose a novel time-varying grey Bernoulli model to investigate the nonlinear, complexity, and time-varying characteristics associated with electric vehicles stock and sales. We first design the time-varying parameters and a power exponent to explore the nonlinear developing trends of sequences. Subsequently, the cuckoo search algorithm determines optimum solutions because of its competence in dealing with complex optimization problems. Furthermore, its relationship with existing grey prediction models is presented, which demonstrates the flexibility and practicality of the newly-designed model. In order to validate this new model, the global electric vehicles stock and electric vehicles sales in France are predicted in comparison with six benchmark models. As demonstrated by the empirical findings, the proposed model is superior in terms of its capacity for forecasting, confirming its significant potential as a promising tool for electric vehicles stock and sales prediction.

A novel discrete multivariable grey model with spatial proximity effects for economic output forecast

The temporal and spatial characteristics of the collected data typically play an essential role in time series forecasting. However, traditional grey models pay more attention to temporal information, meanwhile disregarding the presence of spatial features critical to producing accurate forecasts. Moreover, the grey theory has few attempts to overcome this problem. Given this situation, this paper proposes a novel multivariable grey prediction model considering the spatial proximity effect for time series forecasting. More specifically, the complete spatial distance index is defined initially based on the geographical and economic distance to quantify the intensity of the spatial proximity impact. On this basis, the spatial proximity effect term is constructed and incorporated into the conventional discrete multivariable grey model, establishing the novel model. Subsequently, the particle swarm optimization algorithm determines the optimal weight coefficient in the developed index. For illustration and verification purposes, the proposed model performs experiments predicting Jiangsu's economic outputs compared to five prevalent benchmarks. Furthermore, three statistical metrics, the Diebold-Mariano test, the Monte Carlo experiments, and the probability density analysis are employed to validate the model's efficacy and robustness. Empirical results demonstrate that the proposed method outperforms all five competitors with strong robustness and broad generalization. Therefore, this new model is used for forecasting Jiangsu's future economic outputs from 2021 to 2025.

A summary of grey forecasting models

PurposeThe purpose of this paper is to summarize progress of grey forecasting modelling, explain mechanism of grey forecasting modelling and classify exist grey forecasting models.Design/methodology/approachGeneral modelling process and mechanism of grey forecasting modelling is summarized and classification of grey forecasting models is done according to their differential equation structure. Grey forecasting models with linear structure are divided into continuous single variable grey forecasting models, discrete single variable grey forecasting models, continuous multiple variable grey forecasting models and discrete multiple variable grey forecasting models. The mechanism and traceability of these models are discussed. In addition, grey forecasting models with nonlinear structure, grey forecasting models with grey number sequences and grey forecasting models with multi-input and multi-output variables are further discussed.FindingsIt is clearly to explain differences between grey forecasting models with other forecasting models. Accumulation generation operation is the main difference between grey forecasting models and other models, and it is helpful to mining system developing law with limited data. A great majority of grey forecasting models are linear structure while grey forecasting models with nonlinear structure should be further studied.Practical implicationsMechanism and classification of grey forecasting models are very helpful to combine with suitable real applications.Originality/valueThe main contributions of this paper are to classify models according to models' structure are linear or nonlinear, to analyse relationships and differences of models in same class and to deconstruct mechanism of grey forecasting models.

Prediction of the Share of Solar Power in China Based on FGM (1,1) Model

In recent years, fossil energy reserves have decreased year by year, and the development and use of renewable energy has attracted great attention of governments all over the world. China continues to promote the high-quality development of renewable energy such as solar power generation. Accurate prediction of the share of solar power in China is beneficial to implementing the goals of carbon peaking and carbon neutralization. According to the website of China’s National Bureau of statistics, the earliest annual data of China’s solar power generation is 2017, which leads to there being very few data on the share of China’s solar power generation. Therefore, the prediction accuracy of most prediction methods is low, and the advantages of the grey prediction model are shown. Based on the share of solar power in China from 2017 to 2020, this paper constructs an FGM (1,1) model, calculates r using the Particle Swarm Optimization (PSO) algorithm, and predicts the share of solar power in China in the next few years. r = 0.3858 and MAPE = 0.20% were obtained by calculation of the model. The prediction results show that the share of solar power generation in China will increase year by year, and it will reach about 4.2301% by 2030. In addition, it is found that the share of China’s solar power generation in 2021 is 2.1520%, and the predicted value is 2.1906%. It can be seen that the prediction error is small. Finally, the limitations and future research directions are elucidated. The prediction results presented in this paper will help to guide the development of solar power generation in China, and are of great significance in speeding up the pace of energy structural adjustment, accelerating the construction of a clean, low-carbon, safe and efficient energy system, and promoting sustainable development.

An improved grey multivariable time-delay prediction model with application to the value of high-tech industry

To analyse the time lag effects between independent variables and dependent variables, we propose a discrete time-delay grey multivariable modelDMTGM1,Nτ. There are three improvements in this new model compared to the existing models. First, the time lag parameters are assigned different values for each independent variable. A linear correction term expands the new model. Second, with the given time lag, the least square method can be used to calculate the parameter vector. The time response function of DMTGM1,Nτ is generated, which has the advantage of eliminating the jumping errors between discrete and continuous functions over the existing grey forecasting models. Third, all of the feasible combinations of the time lag parameters are compared by using a traversal algorithm to identify the best values with the minimized mean absolute percentage error (MAPE). In three different case studies, the performance of the new model is evaluated and compared to that of a number of mainstream grey models as well as non-grey models. According to the findings, the newly designed model performs significantly better than the compared models.

An Optimized Damping Grey Population Prediction Model and Its Application on China's Population Structure Analysis.

Population, resources and environment constitute an interacting and interdependent whole. Only by scientifically forecasting and accurately grasping future population trends can we use limited resources to promote the sustainable development of society. Because the population system is affected by many complex factors and the structural relations among these factors are complex, it can be regarded as a typical dynamic grey system. This paper introduces the damping accumulated operator to construct the grey population prediction model based on the nonlinear grey Bernoulli model in order to describe the evolution law of the population system more accurately. The new operator can give full play to the principle of new information first and further enhance the ability of the model to capture the dynamic changes of the original data. A whale optimization algorithm was used to optimize the model parameters and build a smooth prediction curve. Through three practical cases related to the size and structure of the Chinese population, the comparison with other grey prediction models shows that the fitting and prediction accuracy of the damping accumulated–nonlinear grey Bernoulli model is higher than that of the traditional grey prediction model. At the same time, the damping accumulated operator can weaken the randomness of the original data sequence, reduce the influence of external interference factors, and enhance the robustness of the model. This paper proves that the new method is simple and effective for population prediction, which can not only grasp the future population change trend more accurately but also further expand the application range of the grey prediction model.

A novel grey prediction model with a feedforward neural network based on a carbon emission dynamic evolution system and its application.

The objective and accurate prediction of carbon dioxide emissions holds great significance for improving governmental energy policies and plans. Therefore, starting from an evolutionary system of carbon emissions, this paper studies the evolution of the system, establishes a grey model of the system, and expands the modeling structure of this model. The modeling mechanism of the classical feedforward neural network model is organically combined with the function of the external influencing factors of carbon emissions, and the grey model of the carbon emission dynamic system is established with a neural network. Then, the properties of the model are studied, the parameters of the model are optimized, and the modeling steps are obtained. Finally, the validity of the model is analyzed by using the carbon emissions of Beijing from 2009 to 2018. The results of the four cases show that the simulation and prediction errors of the new model are all less than 10%, and case 1 shows the best results of 1.56% and 2.07%, respectively, which are used to predict the carbon dioxide emissions in the next 5 years in Beijing. The prediction results are in accordance with the actual trend, which indicates the effectiveness and feasibility of the model.

Early Warning for Manufacturing Supply Chain Resilience Based on Improved Grey Prediction Model

In a dynamic, uncertain environment, increased supply chain resilience can improve business quality. Predicting changes in enterprise supply chain resilience can help enterprises adjust their operational strategy timeously and reduce the risk of supply and demand interruption. First, a comprehensive resilience assessment framework for manufacturing enterprises was constructed from the perspective of the supply chain, and an improved technique for order of preference by similarity to the ideal solution (TOPSIS) method was used to quantify the resilience level. Considering that the resilience index is easily affected by uncertain factors, and this produces large fluctuations, the buffer operator and metabolism idea are introduced to improve the grey prediction model. This improvement can realize dynamic tracking of the enterprise resilience index and evaluate changes in the enterprise resilience level. Finally, through the analysis of the supply chain data of a famous electronic manufacturing enterprise in China over a two-and-a-half-year period, the results show that the improved TOPSIS method and the improved grey prediction model are effective in improving the supply chain resilience of manufacturing enterprises. This study provides a reference method for manufacturing enterprises to improve their supply chain resilience.

Forecasting Installation Capacity for the Top 10 Countries Utilizing Geothermal Energy by 2030

Foresight of geothermal energy installation is valuable for energy decision-makers, allowing them to readily identify new capacity units, improve existing energy policies and plans, expand future infrastructure, and fulfill consumer load needs. Therefore, in this paper, an improved grey prediction model (IGM (1,1)) was applied to perform the annual geothermal energy installation capacity prediction for the top 10 countries based on installed power generation capacity evaluated at the end of 2021, namely the United States, Indonesia, Philippines, Turkey, New Zealand, Mexico, Italy, Kenya, Iceland, and Japan, for the next nine years for the period from 2022 through 2030. These data can be used by future researchers in the field. Separately, datasets from 2000 to 2021 were collected for each country’s geothermal energy installation capacity to build a model which can accurately predict the annually geothermal energy installation capacity by 2030. The IGM (1,1) model used a small dataset of 22 data points, with one point denoting one year (i.e., 22 years), to predict the capacity of geothermal energy installations for the next nine years. Following that, the model was implemented for each dataset in MATLAB, where appropriate, and the model accuracy was evaluated. Ten separate geothermal energy installation capacity datasets were used to validate the improved model, and these datasets further demonstrated the overall improved model’s accuracy. The results prove that the prediction accuracy of the IGM (1,1) model outperforms the benchmark conventional GM (1,1) model, thereby enhancing the overall accuracy of the GM (1,1) model. The IGM (1,1) model ensures error reduction, suggesting that it is an effective and promising tool for accurate short-term prediction. The results reveal the 2030 geothermal energy installation capacity rankings.

Using grey Gompertz model to explore the carbon emission and its peak in 16 provinces of China

As the largest carbon emitter in the world, China's carbon emissions and the time of carbon peak have always been a hot topic at home and abroad. However, in previous studies, the predicted period of carbon emissions is relatively short. In order to obtain the predicted data of China’s carbon emission and the specific time of carbon peak, the carbon emissions in 30 provinces of China are predicted and analyzed. For 16 provinces with increasing carbon emissions, a novel grey model is proposed to predict the limit of growth data. This model improves the structure of the traditional grey prediction model based on the Gompertz's law and the differential information principle. Its predicted period is longer than that of the traditional grey model. The predicted trend is more in line with the law of natural development. Moreover, this model can predict the limits of a variety of growth data, such as the emissions of various air pollutants. After the prediction, the carbon emissions in these 16 provinces from 2020 to 2032 are obtained. According to the forecast results, eight of the 16 provinces will reach the carbon peak by 2030, namely Jiangsu, Guangdong, Shanxi, Liaoning, Hubei, Guizhou, Guangxi and Jiangxi. The five provinces namely Shandong, Hebei, Anhui, Shaanxi and Fujian will reach the carbon peak in 2031 or 2032. The three provinces namely Inner Mongolia, Xinjiang and Ningxia will not reach their carbon peak by 2032. Based on the above forecasts and analysis, it can be inferred that China will reach the carbon peak in 2032, with emissions of 11.8 billion tons. Scientific predicted data will play a guiding role in carbon emission control. At the end of the paper, the current situations of carbon emissions in 30 provinces are analyzed and suggestions on carbon emission control are put forward.

Empirical Research for Investment Model Based on VMD-LSTM

Investment diversification has become an inevitable trend with the development of the world economy. In this work, we first compare the K-Nearest Neighbor model, the Artificial Neural Network model, the grey prediction model, and the LSTM (Long Short-Term Memory Networks) prediction model for a period of data analysis. The experimental results show that LSTM is superior, and thus LSTM model is selected to forecast the long-term prices in this work. Then, we introduce some indicators, such as convergence divergence ratio and risk coefficient to qualitatively analyze the market price. The five-day moving average method is used to formulate the best trading strategy based on the above-introduced indicators. We apply the commonly used regression indicators ( R 2 and R M S E ) to verify the reliability of the prediction model. Then we introduce new strategies to compare the performance of different ones with them. We found that the five-day moving average method achieved 20% higher returns than the other strategies we used for comparison. Considering the fact that transaction costs may change, we perform the polynomial fitting based on existing strategies by changing the commission cost. The results show that a 1% increase in the gold commission will reduce the total return by 24% to 25%, while a 1% increase in the bitcoin commission will only reduce the total return by 7% to 8%.

Research on the coupling coordination and prediction of industrial convergence and ecological environment in rural of China

Coordination of rural industrial convergence and ecological environment is an important issue in rural social and economic development. This study aims to introduce a comprehensive evaluation index system and construct an integrated approach to evaluate and predict the coupling coordination between rural industrial convergence and ecological environment. Firstly, an index system including 40 indexes is developed for evaluating the two subsystems, and then entropy weight method is applied to determine the weights. Finally, a coupling coordination model and a grey prediction model are developed using provincial panel data from 2017 to 2021 for 29 provinces in China. There are three main findings: 1) penetration of new technologies and agricultural industrialization are the main driving factors of rural industrial convergence. In the three dimensions of ecological environment, environmental pressure has been the smallest contributor, implying that environmental pressure in rural China still needs attention in the next decade. 2) the coupling coordination between rural industrial convergence and ecological environment shows a fluctuating upward trend in China during the studied period and will continue to rise in the next 12 years. Among the 29 provinces studied, only 4 eastern developed provinces have reached primary coordination. The forecast data suggests that coordination with ecological environment will promote the development of rural industrial convergence. 3) the spatial differences of the coupling coordination are related to the level of economic and cultural development, application of agricultural technology, convergence of information technology, energy consumption structure and stock of natural resources. In conclusion, the research develops a rational index system and an effective approach to measure and predict the coupling coordination between rural industrial convergence and ecological environment, providing a theoretical basis for the environmental considerations in the rural industrial convergence policy-making.

Has China’s coal consumption really peaked?—Prediction and scenario analysis of China’s coal consumption peak under the double-carbon target

After China’s “Double Carbon” strategy was proposed to reduce energy and consumption, the optimization of China’s energy consumption structure and whether China’s coal consumption have already reached its peak once again attracted attention. This paper uses the theory of system dynamics to build a system dynamics model of coal consumption. Based on data analysis and the grey prediction model, three categories and nine sub-scenarios are established and the peak coal consumption is analyzed under each scenario. The results show that: The peak of coal consumption in China can echo the strategic goal of peaking carbon in 2030, which is in line with the laws of reality. The economic growth rate has the greatest influence on the coal consumption peak, followed by industrial structure and coal consumption intensity, and the population growth rate has the least influence. The SD3 scenario is the optimal path for China’s future coal consumption development. Under this scenario, coal consumption will peak at 437.8 million tons in 2027. If the future energy consumption trend is close to the SD3 scenario, the socioeconomic growth rate will slow down, the population growth rate will slow down, the industrial structure optimization degree will be higher, and the intensity of coal consumption will be lower. The coal consumption peak varies greatly according to different development goals and transformation dynamics. Under the constraints of the established peak time, the scenario of coal consumption peak has a variety of combinations.

Estimation of forest carbon storage and carbon sink potential in Yunnan Province under the goal of “carbon peaking and carbon neutrality”

Through the accounting of forest carbon reserves and resource value in Yunnan Province, the future forest carbon reserves and carbon sink potential in Yunnan Province are predicted. Using the forest stock expansion method, the forest carbon reserves and their changes in Yunnan Province from 1973 to 2018 are calculated. The GM (1,1) grey prediction model predicts the forest carbon sequestration potential in Yunnan Province. The results show that: (1) the forest carbon storage in Yunnan Province increased from 485.43 million t in 1976 to 937.01 million t in 2018, an increase of 1.9 times, with an average annual growth of 10.50 million t. In 2018, the value of forest carbon reserves was 94062.92 million yuan. (2) The forest carbon sequestration in Yunnan Province varies significantly in different periods. Between 1973-1998 and 1999-2018, the average carbon sequestration was 6.22 million T / A and 16.36 million T / A respectively. (3) The predicted values of forest carbon reserves in 2023, 2028 and 2033 in Yunnan Province are 1001.41 million t, 1103.66 million t and 1216.34 million t respectively, with an average annual increase of carbon sequestration of 17.46 million T / A. According to the research results, the forest area and the carbon storage of forest resources of Yunnan Province is increasing. forest carbon sequestration in Yunnan Province has excellent development potential, and plays a more significant role in forestry carbon sequestration for the realization of the “carbon peaking and carbon neutrality” goal.