Grey Prediction Model Research Articles

Research on Pricing and Replenishment of Vegetables Based on Grey Relational Model and Seasonal Index Prediction Model

This article mainly studies the problem of automatic pricing and replenishment decisions for vegetable products in supermarkets. A grey correlation model and ARIMA time series model are comprehensively established, and based on clustering analysis and regression analysis methods, the distribution patterns of sales volume for each vegetable category and item over time, as well as the relationship with cost plus pricing and mutual correlation, are analyzed to make reasonable predictions for future sales volume. In order to make decisions on product pricing, sales combination, and replenishment, and maximize supermarket profits, the data is preprocessed through normal test and quartile method. Through screening and sorting analysis, the distribution patterns of sales volume for each vegetable category and item over time are obtained; In order to analyze the relationship between different categories of vegetables and between different single products, a grey correlation model is established to determine the analysis sequence, and the correlation coefficients between each category and each single product are calculated to analyze the mutual relationship. And combined with the hierarchical clustering analysis method, the vegetable categories were divided into three sales types: high, medium, and low, expanding the relationship between categories. At the same time, a periodic chart was drawn to determine the periodicity of sales volume distribution in each category. Then, statistical analysis was conducted on the data to determine the linear correlation between sales volume and cost plus pricing. Based on this, a linear regression model was established to analyze the linear relationship between sales volume and cost plus pricing for the six categories; In order to provide the total daily replenishment amount and pricing strategy for the next week, this article establishes a seasonal index prediction model for sales forecasting. Combining the seasonal distribution of each category, the seasonal index is used to predict the sales volume for the next week. Considering the goal of maximizing profits, the target function is listed to comprehensively analyze the replenishment and pricing strategy for the next week.

Research and application of a novel grey multivariable model in port scale prediction under the impact of Free Trade Zone

Purpose Considering the impact of the Free Trade Zone (FTZ) policy on forecasting the port cargo throughput, this paper constructs a fractional grey multivariate forecasting model to improve the prediction accuracy of port cargo throughput and realize the coordinated development of FTZ policymaking and port construction.Design/methodology/approach Considering the effects of data randomization, this paper proposes a novel self-adaptive grey multivariate prediction model, namely FDCGM(1,N). First, fractional-order accumulative generation operation (AGO) is introduced, which integrates the policy impact effect. Second, the heuristic grey wolf optimization (GWO) algorithm is used to determine the optimal nonlinear parameters. Finally, the novel model is then applied to port scale simulation and forecasting in Tianjin and Fujian where FTZs are situated and compared with three other grey models and two machine learning models.Findings In the Tianjin and Fujian cases, the new model outperforms the other comparison models, with the least mean absolute percentage error (MAPE) values of 6.07% and 4.16% in the simulation phase, and 6.70% and 1.63% in the forecasting phase, respectively. The results of the comparative analysis find that after the constitution of the FTZs, Tianjin’s port cargo throughput has shown a slow growth trend, and Fujian’s port cargo throughput has exhibited rapid growth. Further, the port cargo throughput of Tianjin and Fujian will maintain a growing trend in the next four years.Practical implications The new multivariable grey model can effectively reduce the impact of data randomness on forecasting. Meanwhile, FTZ policy has regional heterogeneity in port development, and the government can take different measures to improve the development of ports.Originality/value Under the background of FTZ policy, the new multivariable model can be used to achieve accurate prediction, which is conducive to determining the direction of port development and planning the port layout.

Medium- and Long-Term Electric Vehicle Ownership Forecasting for Urban Residents

With the rapid development of electric vehicles (EVs) in Chinese cities, accurately forecasting the number of EVs used by urban residents in the next five years and more long term is beneficial for the government to adjust industrial policies of EVs, guide the rational planning of urban charging facilities and supporting distribution network, and achieve the rational and orderly development of the EV industry. The paper considers the advantages of using the grey GM(1,1) prediction model to predict the short-term ownership of EVs by urban residents. Then, by forecasting the number of EV users in a certain city in the future and predicting the number of private vehicles in the future, the boundary conditions for long-term year ownership of EVs by residents are determined. Combined with historical data and short-term forecast data generated by the grey prediction model, the model parameters that include the innovation coefficient and imitation coefficient of the Bass model are trained using a genetic algorithm. Finally, the Bass model is used for medium- to long-term ownership forecasting from 2023 to 2040. The prediction error for the target year is provided. The simulation results indicate that the ownership of resident EVs in this city will experience rapid growth in the next five years.

Damping accumulative NDAGM(1,N, α) power model and its applications

PurposeIn this essay, a new NDAGM(1,N,α) power model is recommended to resolve the hassle of the distinction between old and new information, and the complicated nonlinear traits between sequences in real behavior systems.Design/methodology/approachFirstly, the correlation aspect sequence is screened via a grey integrated correlation degree, and the damped cumulative generating operator and power index are introduced to define the new model. Then the non-structural parameters are optimized through the genetic algorithm. Finally, the pattern is utilized for the prediction of China’s natural gas consumption, and in contrast with other models.FindingsBy altering the unknown parameters of the model, theoretical deduction has been carried out on the newly constructed model. It has been discovered that the new model can be interchanged with the traditional grey model, indicating that the model proposed in this article possesses strong compatibility. In the case study, the NDAGM(1,N,α) power model demonstrates superior integrated performance compared to the benchmark models, which indirectly reflects the model’s heightened sensitivity to disparities between new and old information, as well as its ability to handle complex linear issues.Practical implicationsThis paper provides a scientifically valid forecast model for predicting natural gas consumption. The forecast results can offer a theoretical foundation for the formulation of national strategies and related policies regarding natural gas import and export.Originality/valueThe primary contribution of this article is the proposition of a grey multivariate prediction model, which accommodates both new and historical information and is applicable to complex nonlinear scenarios. In addition, the predictive performance of the model has been enhanced by employing a genetic algorithm to search for the optimal power exponent.

A new information priority grey prediction model for forecasting wind electricity generation with targeted regional hierarchy

Amidst the global transition to renewable energy electricity generation, this study addresses the need for improved wind electricity generation forecasting, which is pivotal in optimizing energy strategies and meeting sustainability targets. The research introduces an innovative adaptation of the grey prediction methods, named UNAGO-GM (1, 1), which is enhanced by the unified new-information priority accumulating generation operator (UNAGO) that emphasizes more recent data. This advanced model significantly improves the model's adaptability and accuracy in forecasting wind electricity generation within the regional hierarchy. To demonstrate the proposed model’s superior forecasting efficacy, the comparative experiments are conducted in the hierarchical forecasting fashion across national, regional, and global levels by contrasting UNAGO-GM (1, 1) with prevailing benchmarks like ARIMA and LSTM. The results indicate UNAGO-GM (1, 1)’ s highest forecasting accuracy and universality, with 54.07% and 156.48% improvement rates concerning MAPEs and RMSEs across all benchmarks and forecasting levels. Furthermore, UNAGO-GM (1, 1)’s output stability is validated through the robustness tests applying Monte Carlo simulations for the intelligent algorithm stability and matrix perturbation analyses. In conclusion, the novel UNAGO-GM (1, 1) model represents a significant breakthrough in renewable energy forecasting, providing precise and adaptable predictions essential for strategic planning and policy formulation in the transition towards sustainable energy sources.

Making The Best Investment Strategy for Insurance Companies Under The Background of Extreme Weather

In the face of the negative impacts of the frequent occurrence of extreme weather disasters around the world, we have developed natural disaster risk prediction and analysis models for insurance companies to maximize their benefits. This paper first selects six risk indicators, such as flood, cold wave, drought and other disasters, and combines the analytic hierarchy process (AHP) and grey prediction model (GM) to calculate the impact weights of six kinds of natural disasters, and predicts the probability of future occurrence, so as to realize the comprehensive assessment of natural disaster risk. This paper also analyzes the effect of the owner's intention to buy insurance on the insurance strategy through the function model, and solves the optimal insurance strategy through the technique for order preferenceby similarity to ideal solution (TOPSIS) and neural network. We define the ratio of the total profit of natural disaster insurance to the total cost as the decision coefficient K. When K is greater than 35%, the insurance company chooses to write the policy, and when K is greater than 5% and less than 35%, the insurance company chooses to bear the risk. Finally, this paper collected the data of Florida and Kyushu, Japan. Through the insurance model, it calculated that the total profit of natural disaster insurance in Florida was 635.8 million US dollars, while that in Kyushu was -61.51 million US dollars. Therefore, insurance companies should invest in Florida.

Establishment of urban green corridor network based on neural network and landscape ecological security

The planning and construction of urban ecological corridors play a role in restoring and improving the ecological environment of cities, promoting the movement of other species and biological factors living in urban environments and wider regions. It also profoundly influences and enriches the spiritual and cultural experiences of people living in cities and nearby spaces. The reason for the emphasis on green corridors is that many cities and towns are constantly expanding, and the urban green space system has not yet formed an effective network. At the same time, factors such as the loss of the urban natural environment, biodiversity reduction, and environmental degradation have led to the need to build urban green corridors to deal with risks. By improving the neural network model, this paper predicted the construction land scale of the urban green corridor network, which was used to adjust the land use structure of the green corridor and optimize the land use layout. This paper aims to use the upgraded neural network method to predict the scale of urban green corridor network building land, which helps to evaluate the ecological security status. It can solve the dynamic solution problem of multi-indicator variable weight problems, overcoming the influence of subjective factors in the weight-setting process. The experiment adopted the improved neural network model for prediction. The results showed that its accuracy was much higher than the gray prediction model, which has improved by about 14.81%. This paper fully proved that the improved neural network model had a high degree of fit and feasibility for predicting the land scale of urban green corridor networks. It is directly related to the rationality and practicability of the urban green corridor network planning scheme, which plays a role in guaranteeing the ecological security of the landscape.

Grey prediction model based on Euler equations and its application in highway short-term traffic flow

Grey prediction model based on Euler equations and its application in highway short-term traffic flow

A novel grey forecasting model with generalised fractal derivative and its optimisation

PurposeThe purpose of this paper is to propose a new grey prediction model, GOFHGM (1,1), which combines generalised fractal derivative and particle swarm optimisation algorithms. The aim is to address the limitations of traditional grey prediction models in order selection and improve prediction accuracy.Design/methodology/approachThe paper introduces the concept of generalised fractal derivative and applies it to the order optimisation of grey prediction models. The particle swarm optimisation algorithm is also adopted to find the optimal combination of orders. Three cases are empirically studied to compare the performance of GOFHGM(1,1) with traditional grey prediction models.FindingsThe study finds that the GOFHGM(1,1) model outperforms traditional grey prediction models in terms of prediction accuracy. Evaluation indexes such as mean squared error (MSE) and mean absolute error (MAE) are used to evaluate the model.Research limitations/implicationsThe research study may have limitations in terms of the scope and generalisability of the findings. Further research is needed to explore the applicability of GOFHGM(1,1) in different fields and to improve the model’s performance.Originality/valueThe study contributes to the field by introducing a new grey prediction model that combines generalised fractal derivative and particle swarm optimisation algorithms. This integration enhances the accuracy and reliability of grey predictions and strengthens their applicability in various predictive applications.

Accelerating renewables: Unveiling the role of green energy markets

The innovative local grey forecasting model for energy production prediction emerges as a pivotal tool in the shift towards renewable energy, embodying the essence of adaptability and precision for the urgent need for sustainable solutions. The integration of such forecasting models is imperative for comprehending the complexities of energy production and market dynamics, ultimately facilitating a seamless transition to a sustainable energy future. Given this, the current study investigates the connections between renewable energy consumption and its determinants, focusing on the role of green energy markets across 12 countries from 2003 to 2021. Employing a novel quantile regression approach, this study analyzes the impact of green energy markets, GDP per capita, urban population, and environmental policies on renewable energy consumption, introducing a renewable energy consumption index for novel insights. Rooted in grey system theory, this study offers a unique approach to predicting energy outputs, crucial for guiding the strategic decisions in green energy markets. The quantile regression analysis reveals that green energy markets have a differential impact on renewable energy consumption whereas Renewable Energy Installed Capacity positively affects renewable energy consumption across all quantiles. Environmental Stringency Policies consistently show a negative impact across all quantiles. By providing reliable predictions of energy production, these findings empower policymakers, investors, and operators to make strategic, data-driven decisions that align with the fluctuating demands of the market and the overarching goals of sustainability and environmental stewardship.

Evaluation and prediction of water security levels in Northwest China based on the DPSIR model

Since the the dawn of the 21st century, water security has occupied a pivotal position in fostering sustainable development. However, frequency of extreme weather events due to climate change and higher intensity of anthropogenic events, water security problems in Northwest China are becoming increasingly prominent. This paper elucidates water security levels and predicts future trends of Northwest China. Firstly, the Driving Force-Pressure-State-Impact-Response (DPSIR) conceptual framework served as the foundation for the establishment of the water security evaluation index system. Then used the entropy weight method to calculate the weight of indicators and the five-element connection degree method was used to assess the water security levels. The period for the data used in the study was from 2010 to 2019. Finally, a grey prediction model was employed to forecast the water security levels from 2023 to 2030. The findings showed that: (1) From 2010 to 2019, the water security situation gradually developed to a very high level in Northwest China: Shaanxi Province mostly attained a high level, and the water security grades of Ningxia Hui Autonomous Region and Xinjiang Uygur Autonomous Region were mostly at a very low level. (2) Generally, from 2023 to 2030, the water security level will continue to improve. The water security grades of Shaanxi, Gansu, and Qinghai will mostly be at the medium level, while that of Ningxia Hui Autonomous Region and Xinjiang Uygur Autonomous Region will be at the low level. (3) The five dimensions of water security in Northwest China demonstrated a fluctuating yet consistently upward trend. (4) Based on the results, suggestions were put forward regarding water security and sustainable development in Northwest China.

Prediction of water consumption in Beijing based on the multi-variable grey model with adjacent accumulation

Abstract With the rapid development of the social economy, the importance of water resources is becoming increasingly prominent. Urban water demand in Beijing has been growing rapidly. Accurate water consumption forecasting is of utmost importance for reasonable allocation and optimization of water supply systems. In this study, an innovative multi-variable grey prediction model with adjacent accumulation (AOGM(1,N)) is proposed to predict Beijing's annual water consumption for four different water usage scenarios (domestic water, agricultural water, industrial water, and environmental water) by incorporating the adjacent accumulation into the optimized grey model. Grey relational analysis is used to select the key influence factors. The adjustable parameter of the prediction model is chosen by using the particle swarm optimization algorithm. By comparing with other models in the existing literature, the proposed AOGM(1,N) model has evidently superior prediction performance based on the error indicators, which supports the novel method's merits and validity. This study could help us better understand water usage and be applied to the planning and management problems of urban water supply systems.

Application of Grey Prediction Models for Efficient Traffic Flow Prediction

Traffic flow prediction is crucial in transportation management and optimizing control strategies and infrastructure planning. However, traditional prediction methods often need help handling traffic data's inherent uncertainty and complexity. This paper explores the application of Grey prediction models as a promising approach for efficient traffic flow prediction. Grey prediction models, rooted in Grey systems theory, offer a robust framework for modelling systems with incomplete and uncertain information. The paper provides a comprehensive overview of Grey prediction models, including the Grey Verhulst, Grey-Markov, and Grey differential equation models. It discusses the process of data collection, pre-processing, and model application in traffic flow prediction. Case studies and experimental results demonstrate the effectiveness of Grey prediction models in accurately forecasting traffic flow patterns. Despite challenges such as data sparsity and model complexity, Grey prediction models offer significant advantages over traditional methods, including improved prediction accuracy and adaptability to dynamic traffic conditions. The paper concludes with recommendations for practitioners and researchers interested in leveraging Grey prediction models for efficient traffic management.

A novel grey prediction model with four-parameter and its application to forecast natural gas production in China

Due to the non-homology problem and the simple structural characteristics, a grey prediction model will have defects in modeling. In this paper, the structure of the GM(1,1,x(1)) model is deformed, and additional parameters are added. A novel four-parameter grey prediction model NFGM(1,1) is established to avoid the non-homology problem. The accumulation order of the NFGM(1,1) model is optimized to enhance its performance. This paper first introduces a nonlinear term and a linear term into the GM(1,1,x(1)) model to compensate for its structural defects, which can enhance the accuracy of the model in modeling complex modeling sequences. Secondly, a simplified basic formula of the model is proposed to estimate its parameters and iteratively establish the model, which can avoid the problem of non-homologous errors during modeling. Then a novel four-parameter grey prediction model NFGM(1,1) is constructed. Thirdly, the unbiasedness of NFGM(1,1) is proved and verified by matrix theory. Fourthly, by optimizing the order of the NFGM(1,1) model, the model is more flexible and adjustable, and a novel fractional-order four-parameter grey prediction model FNFGM(1,1) can be obtained. Finally, the FNFGM(1,1) model is applied to the prediction of natural gas production in China. The model results show that the FNFGM(1,1) model exhibits superior performance compared to the NFGM(1,1), TWGM(1,1), TDGM(1,1), DGM(1,1), and GM(1,1) models, with the mean relative simulation/prediction/comprehensive percentage errors of 0.92%/1.42%/1.07%, respectively. According to the predicted results, China's natural gas production will reach 3542.9 × 108 m3 in 2027 and some relevant policy recommendations are put forwarded.

New growth in cross-border E-business: evidence from gray forecasting to cross-border E-business in China

PurposeThis paper delves into cross-border E-business, unraveling its intricate dynamics and forecasting its future trajectory.Design/methodology/approachThis paper projects the prospective market size of cross-border E-business in China for the year 2023 using the GM (1,1) gray forecasting model. Furthermore, to enhance the analysis, the paper attempts to simulate and forecast the size of China’s cross-border E-business sector using the GM (1,3) gray model. This extended model considers not only the historical trends of cross-border E-business but also the growth patterns of GDP and the digital economy.FindingsThe forecast indicates a market size of 18,760 to 18,934 billion RMB in 2023, aligning with the consistent growth observed in previous years. This suggests a sustained positive trajectory for cross-border E-business.Originality/valueCross-border e-commerce critically shapes China’s global integration and traditional industry development. The research in this paper provides insights beyond statistical trends, contributing to a nuanced understanding of the pivotal role played by cross-border e-commerce in shaping China’s economic future.

Decoupling carbon emissions from the economy and low-carbon ecological city construction-based on 196 cities in China

In pursuit of carbon peaking and carbon neutrality, significant efforts have been directed towards economic, policy, and technical dimensions in China. This study mainly focuses on the decoupling of urban carbon emissions from economic development, and the low-carbon ecological cities (LCEC) construction, aiming at the early realization of high-quality development of urban carbon peaking and carbon neutrality goals. We firstly calculated the Tapio decoupling index of economic development and carbon emission, and the construction level of low carbon ecological cities in 196 cities, then constructed a panel quantile regression model to empirically analyze the main influencing factors of the LCEC construction, and finally predicted the LCEC construction levels in 2030 by using a gray prediction model. The research results show that (1) the majority of the cities achieved carbon emissions decoupling between 2005 and 2019, although a few cities in the Northeast still exhibit negative decoupling. (2) The levels of LCEC construction are notably higher in the eastern and southern regions compared to the west and north, with cities showing higher LCEC construction levels also demonstrating more robust decoupling. (3) Promoting carbon reduction and economic development is crucial for enhancing the LCEC construction levels. However, there are regional differences in the impact factors such as housing investment, energy endowment, foreign investment, and environmental focus on the LCEC construction levels within each economic zone. Therefore, each economic zone should formulate tailored development strategies based on its unique characteristics. (4) The Grey prediction model predicts that 69 cities will achieve medium to high levels of LCEC construction by 2030. These results would provide policy recommendations for local governments to promote comprehensive and sustainable urban development.

A novel dynamic grey multivariate prediction model for multiple cumulative time-delay shock effects and its application in energy emission forecasting

Energy emission systems are often influenced by external information and policy shock. However, conventional grey models ignore the existence of the impact of multiple shock events and cumulative time-delay effects, which are crucial for achieving accurate forecasts. From a dynamic perspective, we aim to establish a multivariable grey prediction model considering the impact of multiple shock events, namely MSGTDM(1,N) model. Specifically, to accurately describe the impact of different types of external shocks on the system, three kinds of nonlinear dynamic shock functions are designed, including growth dynamic shock function, decline dynamic shock function, and slope dynamic shock function. Based on this, multi-dimensional instantaneous shock utility terms and cumulative time-delay utility terms are constructed to accurately measure shock effects. And Whale Optimization Algorithm is employed to determine the optimal parameters of the shock functions through comprehensive comparative analysis. The findings affirm the MSGTDM(1,N) model's higher predictive accuracy and validity. Consequently, the forecast results of China's carbon emissions from coal and natural gas consumption in 2022–2025 provide a reliable basis for adjusting the energy structure and implementing the “dual-carbon” policy effectively.

A novel local grey model considering fluctuation based on kernel function for energy production prediction

The structure of energy production is constantly evolving, particularly in a low-carbon context, to align with the development of various industries. To pursue the global goal of carbon neutrality, it is essential to strike a balance between traditional energy and clean energy. Thus, it is of great practical significance to forecast different energy production effectively. Firstly, this paper proposes a novel local grey forecasting model to predict different energy production. Energy production has fluctuated in the past few years due to government policies. Secondly, the novel local grey forecasting model focuses on each year’s production in the modeling process and uses a kernel function to measure the validity of the information. Meanwhile, different from other grey models, the dynamic model proposed makes it possible that there are no static parameters for the overall prediction. Then, the window width in the local model is determined using the particle swarm optimization algorithm. Furthermore, energy production prediction including traditional energy and clean energy production in total North America, Europe and Asia Pacific is conducted. Results show that the novel local grey model has satisfactory prediction accuracy compared with other models, whose MAPEs have controlled under 5% for different energy production. Finally, reasonable suggestions for energy production are proposed to meet various energy needs.

A novel seasonal grey prediction model with fractional order accumulation for energy forecasting

To accurately predict sequence data with seasonal characteristics, we combine data restart technology and fractional order accumulation into a novel seasonal grey model (FSGM (1,1, α)). The particle swarm optimization (PSO) algorithm is used to solve the fractional order and background value coefficients of the model, and the effectiveness of FSGM (1,1, α) is verified using three cases. Finally, we use FSGM (1,1, α) to predict quarterly electricity generation in Beijing and Henan Province and quarterly petroleum coke production in China from 2023 to 2027. The research results indicate that, first, FSGM (1,1, α) is reasonable and effective and has the ability to accurately capture the dynamic trend of seasonal data. Second, compared with the grey model (GM (1,1)), seasonal grey model (SGM (1,1)), data grouping grey model (DGGM (1,1)), data grouping seasonal model (DGSM (1,1)), and data grouping seasonal time model (DGSTM (1,1)), which have seasonal characteristics, FSGM (1,1, α) can better fit the original data, achieve higher prediction accuracy, and perform better. Third, from 2023 to 2027, it is predicted that there will be no significant change in Beijing's electricity generation, and the current stable trend will be maintained. Both the power generation in Henan Province and the petroleum coke production in China will steadily increase to a certain extent, with obvious seasonal cyclical fluctuations. Notably, the power generation and petroleum coke production in Henan Province in the fourth quarter of 2027 will increase by 11.50 % and 10.93 %, respectively, compared to those in the fourth quarter of 2023.

External Corrosion Analysis of Gas Pipeline Based on Gray Prediction Models

External Corrosion Analysis of Gas Pipeline Based on Gray Prediction Models