Grey Prediction Model Research Articles

Evaluation of Corrosion Residual Life Prediction Methods for Metal Pipelines.

The analysis of the basic characteristics of various research methods is highly needed to predict the residual life of the pipeline accurately, help managers understand the operational risks, and provide a reference for developing pipeline transportation and maintenance inspection plans and anti-corrosion measures. Based on a comprehensive investigation of the existing research on the residual life of the pipeline, this paper finds that the current mainstream life prediction method, based on historical statistical data, has the shortcomings of inconsistent modeling methods, inconsistent basic data, and a lack of comparative evaluation among methods. Moreover, considering the in-depth study of BP neural network modeling, grey theory modeling, time series modeling, and exponential smoothing modeling, optimal prediction models using different methods based on the same historical data are established. These optimal modeling methods are discussed, and the feasible modeling path for the accurate prediction of the pipeline’s residual life is given by comparing the prediction accuracy of each model. In addition, the findings serve as a guide for developing an anti-corrosion strategy by highlighting the contribution of the prediction results of the residual life to pipeline decision-making. By comparison, it is found that the accuracy of the four prediction models is as follows: the grey theory prediction model, the exponential smoothing prediction model, the BP neural network prediction model, and the time series prediction model, from high to low, respectively.

Assets Trading Strategy Based on ALG Evaluation and Prediction Integrated Model

With the rapid development of economy and the maturity of financial markets, more and more people choose to invest in stocks to achieve property appreciation, so the study of stock investment decision-making is increasingly important. In this topic, in order to maximize the interests of investors, our team not only needs to accurately predict the future trend of gold and bitcoin, but also needs to make the most reasonable strategy according to the future trend of gold and bitcoin. Therefore, our team reads a large number of papers, refer to the current model of stock trend prediction and decision-making and combine with the data given by the topic to establish our model. According to the stock trend, this paper designs an ALG (Apriori algorithm - Attentional BiLSTM neural network - GARCH model) evaluation and prediction integrated model, and makes a time series chart of daily return rate related to gold and bitcoin, finally predicts the daily return rate. This paper compares the daily yield predicted by ALG model with that predicted by grey model, which shows that the yield predicted by ALG model is much higher than that predicted by grey prediction model.

Pseudo grey metabolic Markov model and its application in urban rainfall prediction

Purpose: The purpose of this paper is to propose a pseudo-grey metabolic grey Markov model to deal with the prediction issue in which the original sequences are oscillation sequences. Design/methodology/approach: First, the original sequences were processed with the accelerated advection transformation and the weighted mean generation transformation to make them smoother. Then, the mean GM (1, 1) model was applied to the multi-step prediction of the pre-processed data sequences. Finally, with the help of the optimal partitioning method, the pseudo-grey metabolic Markov model was used to correct the prediction results and determine the final prediction values. Findings: The results demonstrate that the accuracy of this model is significantly higher than that of the traditional grey Markov model, which further verifies the rationality of the proposed model. Therefore, scientific and reasonable prediction of urban rainfall is of great theoretical significance and application value for the government and decision-making departments to formulate drought prevention and disaster mitigation measures. Originality/value: The model in this paper not only provides new ideas for the data preprocessing problem of the grey Markov model, but also solves the problem of errors due to individual subjectivity in state interval division. It provides a novel idea for the development of grey prediction models. The rationality and validity of the model are illustrated by taking the Zhengzhou City of Henan Province as examples.

An Improved Genetic Algorithm for Location Allocation Problem with Grey Theory in Public Health Emergencies.

The demand for emergency medical facilities (EMFs) has witnessed an explosive growth recently due to the COVID-19 pandemic and the rapid spread of the virus. To expedite the location of EMFs and the allocation of patients to these facilities at times of disaster, a location-allocation problem (LAP) model that can help EMFs cope with major public health emergencies was proposed in this study. Given the influence of the number of COVID-19-infected persons on the demand for EMFs, a grey forecasting model was also utilized to predict the accumulative COVID-19 cases during the pandemic and to calculate the demand for EMFs. A serial-number-coded genetic algorithm (SNCGA) was proposed, and dynamic variation was used to accelerate the convergence. This algorithm was programmed using MATLAB, and the emergency medical facility LAP (EMFLAP) model was solved using the simple (standard) genetic algorithm (SGA) and SNCGA. Results show that the EMFLAP plan based on SNCGA consumes 8.34% less time than that based on SGA, and the calculation time of SNCGA is 20.25% shorter than that of SGA. Therefore, SNCGA is proven convenient for processing the model constraint conditions, for naturally describing the available solutions to a problem, for improving the complexity of algorithms, and for reducing the total time consumed by EMFLAP plans. The proposed method can guide emergency management personnel in designing an EMFLAP decision scheme.

Correlation analysis and prediction of PEM fuel cell voltage during start-stop operation based on real-world driving data

The accurate prediction of fuel cell voltage is critical for fuel cell health management, which directly affects the performance and cost of fuel cell vehicles. This paper proposes analysis and prediction approaches for the start-stop voltage of fuel cell based on data from a proton exchange membrane fuel cell (PEMFC) bus operated under real traffic conditions in Chengdu, China. First, three correlative analysis methods are used to identify the correlation between the PEMFC voltage and other variables during start-stop operation. Then, the original sequence of PEMFC voltage at start-stop is processed using the grey model, residual grey model, logarithmic-power function grey model, and cotangent function grey model. Four grey prediction models are combined and optimized based on a backpropagation neural network (BPNN). Finally, an organic grey BPNN model (OGNNM) is established to predict the PEMFC voltage during start-stop operation and analyze its accuracy. Results indicate that the PEMFC voltage changes sharply during start-stop, which is closely connected to the current, hydrogen gas tank pressure, and PEMFC stack temperature. In addition, the proposed OGNNM has higher prediction precision than the other data-driven models that provides a basis for the control of fuel cell hybrid systems.

Forecasting share of renewables in primary energy consumption and CO2 emissions of China and the United States under Covid-19 pandemic using a novel fractional nonlinear grey model

China and the United States (U.S.) are in the first two places among the countries that consume the most primary energy and emit CO2 emissions in the world. Considering the Sustainable Development Goal 7.2 and Paris Agreement’s goals, forecasting of CO2 emissions and the share of renewables in primary energy consumption for China and the United States in the coming years will be a guide for energy policies. In this context, this study aims to forecast the share of renewables in primary energy consumption and CO2 emissions of China and the U.S. using a novel optimized fractional nonlinear grey Bernoulli model with rolling mechanism, briefly as ROFANGBM(1,1), under pandemic and non-pandemic scenarios. This study also analyzed the gap in the energy consumption and CO2 emissions for the year 2020 caused by the Covid-19 pandemic. The results showed that ROFANGBM(1,1) gave the highest prediction performance with having the lowest mean absolute percentage error (MAPE) value for all cases and the share of renewables in primary energy consumption in 2025 is forecasted as 12.3% for the U.S. and 16.6% for China by using ROFANGBM(1,1). Additionally, CO2 emissions of China and the U.S. are forecasted by using ROFANGBM(1,1) as 10112 Mt and 4583 Mt in 2025, respectively. It is believed that this study will provide new avenues for researchers to make more accurate predictions by addressing the fluctuations due to the Covid-19 pandemic thanks to the proposed novel grey prediction model.

Forecasting China's energy production and consumption based on a novel structural adaptive Caputo fractional grey prediction model

China's total energy consumption and production ranks first in the world. However, China's energy structure is not perfect. Therefore, accurate prediction of future energy trends is of great significance for the Chinese government to adjust the energy structure. Therefore, this paper proposes a novel structural adaptive grey model FCSAGM(p, 1) with Caputo fractional derivative and a new Caputo fractional order accumulation generation operator. Through the comparison between optimization algorithms, Particle Swarm Optimization (PSO) is selected to optimize the parameters of the model. In particular, aiming at the problem of model reliability caused by the optimization algorithm, the robustness of the model is analyzed, and the results show that the model is stable and reliable. Finally, four actual cases of China's total energy consumption, China's total primary energy production, China's thermal power generation and China's hydropower generation are predicted; the prediction result shows that the new model has higher prediction accuracy than the other six grey models.

Integrated fuzzy linguistic preference relations approach and fuzzy Quality Function Deployment to the sustainable design of hybrid electric vehicles

Through the prevalence of sustainable ideas, automobiles are increasingly pursuing environmental protection strategies for green design, the non-traditional hybrid electric vehicles (HEV) are promoted continuously. If the company can add emotional value to the modeling of HEV, it will be helpful to its sustainable design and sales promotion of it. Therefore, an innovative model combining fuzzy linguistic preference relations (FLPR) and fuzzy quality function deployment (QFD) is proposed here to explore the connection between customer sentiment and the front view of the HEV. Compared with the previous methods, FLPR has the advantages of fewer comparison times and high consistency. First, find out the customer’s emotional expectations and attribute weight ranking for HEV through FLPR, and import customer requirements (CRs) on the left side of fuzzy QFD. Second, the grey prediction model was used to screen out the key engineering features (ECs) and the initial weight of HEV. Finally, based on human subjective imprecise natural semantics, fuzzy QFD established a matrix association between CRs and key ECs, then finally obtained the optimal combination of ECs' final weight and morphological design. The results can assist designers to shorten product development cycles and improve customers' emotional satisfaction, which provides a theoretical reference for the sustainable design and marketing of environmentally friendly cars in the future.

A dynamic calculation model of the carbon footprint in the life cycle of hospital building: a case study in China

PurposeThe calculation of buildings’ carbon footprint (CFP) is an important basis for formulating energy-saving and emission-reduction plans for building. As an important building type, there is currently no model that considers the time factor to accurately calculate the CFP of hospital building throughout their life cycle. This paper aims to establish a CFP calculation model that covers the life cycle of hospital building and considers time factor.Design/methodology/approachOn the basis of field and literature research, the basic framework is built using dynamic life cycle assessment (DLCA), and the gray prediction model is used to predict the future value. Finally, a CFP model covering the whole life cycle has been constructed and applied to a hospital building in China.FindingsThe results applied to the case show that the CO2 emission in the operation stage of the hospital building is much higher than that in other stages, and the total CO2 emission in the dynamic and static analysis operation stage accounts for 83.66% and 79.03%, respectively; the difference of annual average emission of CO2 reached 28.33%. The research results show that DLCA is more accurate than traditional static life cycle assessment (LCA) when measuring long-term objects such as carbon emissions in the whole life cycle of hospital building.Originality/valueThis research established a carbon emission calculation model that covers the life cycle of hospital building and considered time factor, which enriches the research on carbon emission of hospital building, a special and extensive public building, and dynamically quantifies the resource consumption of hospital building in the life cycle. This paper provided a certain reference for the green design, energy saving, emission reduction and efficient use of hospital building, obviously, the limitation is that this model is only applicable to hospital building.

Measurement and Forecasting of China's Financial Stress Index in the A-E-G Coupling Perspective

After the outbreak of the global financial crisis in 2008, many scholars began to quantify financial risks and find alternative variables to reflect financial risks better. The financial stress index is a good alternative. In this paper, nine important factors of economic aggregate, economic benefit, and economic structure are selected to construct China's Financial Stress Index (CFSI). Then, the coupling identification of AHP and entropy weight method (A-E) is carried out, and the CFSI and its fluctuation are predicted by the grey dynamic prediction model GM(1, N). The results show that the CFSI constructed by a-E coupling weight can measure the pressure of China's financial system more accurately, and the CFSI is predicted to show A gentle decline trend from 2022 to 2024. Inspired by previous studies by scholars, this paper tries to construct and predict CFSI from the perspective of A-E-G coupling, combining subjective and objective, and hoping to open new ideas on CFSI construction and prediction.

Regional energy forecasting and risk assessment for energy security: New evidence from the Yangtze River Delta region in China

The huge energy consumption for the Yangtze River Delta (YRD) region has caused the increased risks in energy security, thus influencing the achievement of the carbon-neutrality 2060 target in China. To this end, a novel modeling approach based on grey prediction model and cloud model is developed and applied in this paper. By the accuracy measurement, the mean absolute percentage error values of optimal non-linear metabolic grey model (ONMGM (1,1)) are all below 4%, which are lowest in the training data set of YRD region. So, the prediction capability of ONMGM (1,1) outperforms other grey prediction models. Based on the forecasting of ONMGM (1,1), Jiangsu Province has been a highly increasing trend from year 2021–2025. In contrast, the total energy consumption of Shanghai is lower than the other regions. The findings also show that the overall risk level of energy security will change from the medium to the very high in the year 2006–2025. Moreover, the traditional energy security will occupy vital positions for the coming years in the overall energy security of YRD region. Eventually, this study maybe provides some decision support to achieve the early warning of energy consumption and energy security between different regions.

The effect of different prediction methods of population in villages and towns on assessing the suitability of biomass energy development

We investigate herein the development of biomass energy within a study area, to study how changes in population scale affects the suitability of biomass energy development in villages and towns. The analysis is performed by predicting the development trend of the total population and electricity consumption in the study area, and by establishing a biomass potential profit and loss model. With rural areas in western Guangdong as the research object, this study uses three different population prediction models, including the growth rate method, trend extrapolation method, and gray prediction method, to predict the population change trend of the study area in the next decade. The results of the growth rate method indicate a negative population growth rate of 5.28% for the study area over the next 10 years. We used Origin software to conduct polynomial fitting analysis on historical population data, and to make trend extrapolation predictions, the results demonstrate that in the short run, the population maintain a stable decline of approximately 1.7% by 2030. The results of the gray prediction model indicate that the population shows a slight decrease of 0.3‰.We determined the quantity of straw resources and the theoretical power generation potential of regional biomass based on the crop yields of Maoming, Zhanjiang, and Yangjiang, as well as the collectable coefficient and corresponding calorific value of straw in Guangdong Province. The total amount of crop straw resources in western Guangdong is approximately 120 522.6 tons, and the total biomass theoretical power generation potential is 305 × 103 MWh, the goal of 100% biomass energy supply will not be fully realized before 2030. Among these, the biomass energy supply in Maonan District of Maoming City is expected to reach 40–60%, and surplus energy demand may possibly combine with other renewable and traditional energy sources. The southern part of Zhanjiang has slightly less potential for biomass energy development than Maoming, with a maximum potential of 25%. All three prediction models suggest that Yangjiang is not suitable for biomass development in the short term, and its biomass potential will only meet 1% of its energy needs by 2030.

Aero-engine exhaust gas temperature prediction based on adaptive disturbance quantum-behaved particle swarm optimization

Aircraft engine EGT (exhaust gas temperature) is uncertain. In order to reduce the EGT influence to the health of the engine, it is important to carry out the prediction. A novel EGT prediction method based on the combination method is proposed. Firstly, MIV (Mean Impact Value) was used to reduce the dimension of the input numbers. Second, the EGT was predicted by some single models such as BP (back propagation) neural network model, SVR (support vector regression) model, PLS (partial least square) model, GM(1,N) (multi-parameter input gray prediction model), MLR (multiple linear regression) model. Then absolute mean error was used to evaluate the predictive results of single models and the best predictive results of four single methods were selected to establish combination model with PSO (particle swarm optimization). Finally, the combination model was used in predicting the EGT of V2500 aero-engine. Experiment results show that the combination method is more reliable and suitable than the single models for aero-engine EGT prediction.

Forecasting Chinese provincial carbon emissions using a novel grey prediction model considering spatial correlation

In response to the errors caused by the uniform background value coefficients in the traditional grey model and the lack of analysis ability of panel data, this study proposes a two-stage background value calculation method and introduces a spatial weight matrix to employ the spatial correlation of variables in the grey model, creating a spatial grey model SGM(1,1,m) and realizing the modeling of spatial data by a grey model. The validity of the SGM(1,1,m) model was tested using carbon emission data from 30 provinces in China, and the carbon emissions of these provinces are predicted from 2020 to 2025. Conclusions are drawn as follows. First, the two-stage background value optimization mode and the addition of spatial overflow term in the model are reasonable, and the SGM(1,1,m) model improves the modeling performance in a certain sense compared with the GM(1,1) model while realizing regional association modeling. Second, the SGM(1,1,m) model has some formal similarities with the grey multivariate model, but while both are similar in terms of modeling requirements, the modeling purposes or economic meanings represented are different and should not be confused. Third, the SGM(1,1,m) model can achieve modeling predictions while providing a simple analysis of the spatial correlation of carbon emissions. Fourth, the prediction results present that the rise of carbon emissions in eastern China will level off, but the rise of carbon emissions in the central and western China will accelerate, which is largely because of the faster rise of carbon emissions in key provinces such as Shaanxi, Gansu, Ningxia, and Inner Mongolia.

Trading Prediction Model and Strategy Formulation Based on Grey System

In this paper, we study the optimal investment strategy between gold and Bitcoin, establish a market price forecasting system using the grey forecasting model GM (1,1), and calculate the optimal investment between gold and Bitcoin through the association rule algorithm. Optimal investment ratio, and then determine the maximum return. The establishment of predictive investment models helps investors to formulate optimal investment plans. The established model can use certain data from previous years to predict the future exchange rate prices of gold and bitcoin and help trading investors make optimal buying and selling decisions. It has a certain model planning ability for investment in real life and has specific practical significance for trade investors.

The Cultivation of Teamwork Quality from the Perspective of Competitive-Complementary Relations

During the process of starting a business, in an entrepreneur team, there’s not only a collaborative relationship among team members, there’re also competitions between them. To avoid the consequence of team output reduction caused by the competitive behavior of team members to fight for rights and interests, it’s necessary to figure out the influence of the Competitive-Complementary Relations (CCR) among members on the performance of the team, so this paper aims to study the cultivation of teamwork quality of college students in entrepreneur team from the perspective of CCR. At first, relevant indexes were selected from four aspects of team factor, teacher factor, team member factor, and efficacy factor to form an Evaluation Index System (EIS) for assessing the teamwork quality of the college student entrepreneur team; then, this paper employed a combinatorial weighing method to integrate the weights obtained by the Entropy Weight Method (EWM) and the Variation Coefficient Method (VCM), constructed conceptual models for the competitive efficacy and complementary efficacy of the college student entrepreneur team, and introduced the methods for calculating the competitive degree index and complementary degree index of team members in detail. After that, the paper used a gray prediction model GM (1, 1) to predict the teamwork quality of several sample college student entrepreneur teams within the study period based on the completion degree of entrepreneurial tasks, and used a coupling model to analyze the coupling and coordination degree between competitive degree, complementary degree, and team performance. At last, experiment results proved the effectiveness of the proposed EIS and models.

Study on Migration Path of Climate Refugees Based on Grey Prediction and Analytic Hierarchy Process

Earth is undergoing climate changes that are affecting our environment. Some of these changes are easy to adjust to, however, some changes of the climate are severe and require immediate drastic solutions. In this paper, we present a solution to dangers posed by rising sea levels in the Maldives. It is a tropical paradise, but it is vulnerable to storms and tsunamis, and due to slowly rising sea levels it could one day disappear completely! The Maldivians will eventually need to migrate to higher ground somewhere. First, we identify the metrics that will influence the process of the transfer of EDPs, while enabling the preservation of culture and language. Then we establish a grey prediction model to predict future changes in population, and identify when the chosen host country will be completely unfit for human habitation. Next we use the Analytic Hierarchy Process combining with the objective situation and subjective thoughts of EDPs to determine the relationship between factors represented by each variable. Finally, we propose policies considerate of all the possibilities so as to maximize the benefits for all involved parties.

A novel seasonal grey model for forecasting the quarterly natural gas production in China

As a green and clean source of energy, natural gas plays an indispensable role in changing the energy structure and reducing greenhouse gas emissions. In this context, forecasting natural gas production becomes particularly important. In China, natural gas production has obvious seasonal and cyclical characteristics. To this end, a grey prediction model combined with particle swarm optimization (PSO) is proposed: the PSO-based data grouping grey model with a fractional order accumulation (PSO-FDGGM (1,1)). The traditional grey model has shortcomings such as low prediction accuracy and poor adaptability, and its application is limited. Compared with the traditional grey models, this model can be used to predict time series with seasonal characteristics, and the prediction accuracy is higher. Based on the data grouping-based grey modelling method (DGGM (1,1)), this model introduces the modeling method of the grey system model with the fractional order accumulation (FGM (1,1)), that is, combining data grouping with fractional accumulation, and using PSO to optimize fractional-order parameters, which improves the performance and adaptability of the model. In order to verify the effectiveness of the model, the model is compared with the traditional grey model (GM (1,1)), FGM (1,1) model, DGGM (1,1) model, autoregressive comprehensive moving average (ARIMA) model and the PSO-based grey forecasting model (PSOGM (1,1)). The mean absolute percent errors (MAPEs) of the comparison model in the test set are 7.46%, 9.28%, 4.38%, 8.60% and 5.97% respectively, while that of PSO-FDGGM (1,1) model is only 3.19%. The results show that PSO-FDGGM (1,1) has good adaptability and prediction accuracy for seasonally fluctuating sequences. In order to further verify the effectiveness and applicability of PSO-FDGGM (1,1), this paper adopts two ways: adjusting the parameters of PSO algorithm in the new model and applying the new model to the prediction of solar power generation, and the empirical results show that the new model is effective in forecasting seasonal data. Finally, the new model is used to predict the quarterly production of natural gas in China from the third quarter of 2021 to the second quarter of 2024. The forecast results can provide a basis for formulating natural gas production plans and environmental policies.

Research on the Optimal Trading Strategy of Gold and Bitcoin

Market traders trade gold, and Bitcoin is aim to maximize their return. This paper utilizes the grey prediction model to explore the optimal trading strategy and optimize fund allocation based on dynamic programming. In addition, by comparing with other traditional trading strategies, we discover that the grey prediction model can more accurately estimate future prices, enabling the trader to gain steadily growing returns at a low-risk level.

Global Equity Measurement Model

In this paper, we calculate the weight of each index through the entropy weight method and perform a regression analysis to obtain the WRSR value representing the comprehensive strength index (CSI). In addition, based on the salary distribution model, we use the linear asteroid mineral investment model to look forward to the future development trend. Finally, we use the gray prediction model GM(1,1) to predict the proportion of each condition in the next 10 years, and the future change with time is obtained by polynomial fitting.