Grey Prediction Model Research Articles

Supercapacitor module quality prewarning based on the improved whale optimization algorithm and GM (1,1) gray prediction model

Supercapacitor module quality prewarning based on the improved whale optimization algorithm and GM (1,1) gray prediction model

A novel fractional multivariate GM(1,N) model with interaction effects and its application in forecasting carbon emissions from China's civil aviation

PurposeThe purpose is to forecasting carbon emissions from China's civil aviation more accurately, a novel fractional multivariate GM(1,N) model with interaction effects is developed in this paper.Design/methodology/approachFirst, the interaction term, the trend terms are introduced in the grey action term to reflect the influence of the interaction between the system-related variables on the change of the system characteristic variables and the time trend of the system development. Then fractional cumulative generating sequence is used as the modeling sequence to reduce the perturbation of the original data. Finally, in order to effectively find the optimal fraction accumulation generation coefficient, the particle swarm optimization (PSO) is used to determine the emerging coefficient.FindingsExperimental results show that FIEGM(1, N) outperforms other grey prediction models in predicting the carbon emissions of CAAC, which can better solve the problem of multivariate system prediction of small samples with trend interaction effect.Originality/valueBy considering the influence of interactions in the system and the trend of system development in combination with fractional accumulation theory, a new method to improve the prediction performance of the GM(1, N) model is proposed. The model is first applied to the prediction of carbon emission of civil aviation in China.

Data-driven adaptive GM(1,1) time series prediction model for thermal comfort.

In this paper, the future prediction of predicted mean vote (PMV) index of indoor environment is studied. PMV is the evaluation index used in this paper to represent the thermal comfort of human body. According to the literature, the main environmental factors affecting PMV index are temperature, humidity, black globe temperature, wind speed, average radiation temperature, and clothing surface temperature, and there is a complex nonlinear relationship between the six variables. Due to the coupling relationship between the six parameters, the PMV formula can be simplified under specific conditions, reducing the monitoring of variables that are difficult to observe. Then, the improved grey system prediction model GM(1,1) with optimized selection dimension is used to predict the future time of PMV. Due to the irregularity, uncertainty and fluctuation of PMV values in time series, based on the original GM(1,1) time series prediction, an adaptive GM(1,1) improved model is proposed, which can continuously change with time series and enhance its prediction accuracy. By contrast, the improved GM(1,1) model can be derived from the sliding window of the adaptive model through changes in the dataset and get better model grades. It lays a foundation for the future research on the predicted index of PMV, so as to set and control the air conditioning system in advance, to meet the intelligence of modern intelligent home and humanized function of sensing human comfort.

Air passenger flow forecasting using nonadditive forecast combination with grey prediction

Air passenger flow forecasting plays a critical role in managing air transportation. It is important to develop accurate methods for forecasting the flow of passengers to formulate relevant operational plans and the schedule of airports. Even though empirical evidence has shown that forecast combination can improve the prediction accuracy of single-model forecasts, no study in the literature on transportation to date has examined the performance of such combinations to predict the demand for air transport. In light of the usefulness of grey prediction, which does not require applying any statistical test to examine the data at hand, and relationships among forecasts to be reckoned with, this study proposes using the nonadditive Choquet fuzzy integral to nonlinearly synthesize forecasts from four commonly used univariate grey prediction models into composite forecasts. The results of experiments confirmed that the risk arising from the incorrect choice of forecasting models can be eliminated by using forecast combinations. The proposed combination method based on grey prediction significantly outperformed the other competitors considered.

Impact of intelligence on the carbon emissions of energy consumption in the mining industry based on the expanded STIRPAT model

The intelligent construction of the mining industry has become an emerging trend in its future development plans. As a traditional high energy consumption industry, its energy conservation and emissions reduction have become particularly urgent concerns. Therefore, it is of great significance to study the impact of intelligence on the carbon emissions from energy consumption in the mining industry. This paper first uses dynamic indirect energy emissions factors to build an accounting model for carbon emissions from energy consumption in the mining industry and calculates the related carbon emissions from 2001 to 2020 by sector. Then, an expandable stochastic environmental impact assessment (STIRPAT) model is established to analyze the impact of intellectualization on the carbon emissions intensity of China’s mining industry. At the same time, many other important factors are included in the model, and the model is fitted using ridge regression. The results show that the order of influence, from large to small, is as follows: energy intensity, industrial output value, per capita output value, the proportion of output value of large and medium-sized enterprises, per capita stock of fixed assets, average enterprise output value, R&D intensity and proportion of indirect energy. Each 1% increase in the above factors will increase the corresponding carbon emissions intensity by 0.2005%, −0.1271%, −0.1207%, 0.7467%, −0.0595%, −0.09813%, 0.1143% and −0.1419%, respectively. Then, the prediction values of the independent variables are obtained by using the scenario analysis method and grey prediction model, and 12 scenarios are set up to predict the carbon emissions intensity of the mining industry energy consumption from 2021 to 2035 by using the expanded STIRPAT model. By analyzing the prediction results, we conclude that Scenario 8, Scenario 9, Scenario 10, Scenario 11 and Scenario 12 can achieve the emissions reduction goals of the mining industry in 2025 and 2030; the optimal path of emissions reduction is given for various scenarios. Finally, a development strategy for emissions reduction in China’s mining industry is proposed.

Carbon emissions predicting and decoupling analysis based on the PSO-ELM combined prediction model: evidence from Chongqing Municipality, China.

The "14th Five-Year Plan" period is a crucial phase for China to achieve the goal of carbon peaking and carbon neutrality (referred to as the "double carbon"). Thus, it is very important to analyze the main factors affecting carbon emissions and accurately predict the change of carbon emissions to achieve the goal of double carbon. For the slow data updates and the low accuracy of traditional prediction models about the carbon emissions, the key factors of carbon emissions change selected by gray correlation method and the consumption of coal, oil, and natural gas were input into four single prediction models: gray prediction model GM(1,1), ridge regression, BP neural network, and WOA-BP neural network to obtain the fitted and predicted values of carbon emissions, which serve as input to the particle swarm optimization-extreme learning machine (PSO-ELM) model together. Based on the PSO-ELM combined prediction method above and the scenario prediction indicators constructed according to relevant policy documents of Chongqing Municipality, the carbon emission values of Chongqing Municipality during the 14th Five-Year Plan period are predicted in this paper. The empirical results show that carbon emissions of Chongqing Municipality still maintain an upward trend, but the growth rate slow down compared with 1998 to 2018. In general, the carbon emission and GDP of Chongqing Municipality showed a weak decoupling state during 1998 to 2025. By calculation, the PSO-ELM combined prediction model is superior to the above four single prediction models in carbon emission prediction and has good property by the robust testing. The research results can enrich the combined prediction method about the carbon emissions and provide policy suggestions for Chongqing's low-carbon development during the 14th Five-Year Plan period.

Multivariate Grey Prediction Model Application in Civil Aviation Carbon Emission Based on Fractional Order Accumulation and Background Value Optimization

The GM(1,N) model, as a classical multivariate grey prediction model, can make a holistic and dynamic analysis of multiple factors and reflect the dynamic change relationship between the variable series and the related factor series. However, numerous works in the literature show that the GM(1,N) model has mechanistic defects, parametric defects, and structural defects. Therefore, the thesis establishes the OGM(1,N) model based on the GM(1,N) model by adding the linear correction term and the amount of grey action. According to the principle of dynamic optimization, the PSO algorithm is used to determine the background value. On this basis, the fractional order idea is introduced to push the model order from the integer field to the real field, and the FOBGM(1,N) model is established to systematically reduce the model error. Second, the literature in the ScienceDirect database for the last ten years is reviewed, and the carbon emission impact factors of civil aviation are selected. The calculated carbon emission values are taken as sample data based on Method 2 of Civil Aviation in Volume 2 of the 2006 IPCC Guide to National Greenhouse Gas Inventories. The results show that the prediction accuracy of the model has an increasing trend after multi-layer and multi-angle optimization. Among them, the MAPE of the OGM model and FOBGM model decreased by 24.40% and 31.86% compared with the GM(1,N) model. The 5-year average prediction accuracy of the FOBGM model reaches 99.996%, which verifies the effectiveness and practicality of the model improvement and has certain practical significance and application prospects.

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

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

Analysis and Evaluation of Global Temperature Changes and the Prediction of Future Global Temperature

Temperatures have continued to rise in recent years, many areas have shown extreme temperatures, and global warming has been a perennial topic. We use various optimization prediction and optimization evaluation methods in this paper to determine the trends and causes of global temperature variation and make effective suggestions for them. We first constructed a first-order differential temperature model and found that the temperature rise in March 2022 was larger than that observed in the past 10 years. By constructing a prediction model (BP) based on BP neural network and a grey temperature prediction model (GAGM) optimized based on adaptive genetic algorithm, and by importing the data, we find that the average temperature in 2116 and 2145 will reach 20℃. In addition, if the correlation factor evaluation model (FAEM) is constructed in the country, and we conclude that there is a correlation between location, time and temperature. Next, we analyzed key environmental factors, such as volcanic eruptions, forest fires, and COVID-19. We imported the CO 2 concentration and temperature data into the FAEM evaluation model to obtain the results that natural disasters can lead to the temperature increase. Then nine environmental factors, including COND, NYKC, NYXH, GDP, PJRK, LJL, LZMJ, KJSP, and GJJY. Taking these factors as independent variables and temperature as the dependent variables, COND, NYXH, PJRK, LJMJ, JSL are the main causes of global temperature change and KJSP. This study provides an assessment of future global temperature changes, making recommendations for policymakers to optimize the energy mix and plant trees to effectively mitigate the global temperature rise.

Will the Wave of Early Retirement of Teachers in Malaysia Continue? - a Grey Prediction Based on GM(1,1) Gray Prediction Model

The number of teachers in Malaysia has decreased since 2017 as a result of a tendency toward early retirement among teachers. Numerous organizations have suggested that laws be passed in reaction to this trend to lessen the effects, but Malaysia has yet to come up with any viable solutions. Is it possible that these circumstances will allow for a progressive decrease in the number of early retirees in the near future? Based on this inquiry, a GM(1,1) gray prediction model was created and a gray prediction was formed, which indicates that in 2022–2024, Malaysia's teacher population and teacher–student radio will continue to decline. The Malaysian government needs to take this seriously and aggressively implement a number of steps to prevent the negative effects of early teacher retirement on educational standards.

Identification and prediction of climate factors based on factor analysis and a grey prediction model in China.

Identifying and predicting the impacts of climate change are crucial for various purposes, such as maintaining biodiversity, agricultural production, ecological security, and environmental conservation in different regions. In this paper, we used the surface pressure (SP), surface temperature (ST), 2-m air temperature (AT), 2-m dewpoint temperature (DT), 10-m wind speed (WS), precipitation (PRE), relative humidity (RH), actual evapotranspiration (ETa), potential evapotranspiration (ETP), total solar radiation (TRs), net solar radiation (NRs), UV intensity (UVI), sunshine duration (SD), convective available potential energy (CAPE) as factors in our climate modeling. The spatiotemporal distribution characteristics of the climate factors were analyzed and identified based on historical data for China from 1950 to 2020 using factor analysis and a grey model (GM (1,1)), and their future change characteristics were predicted. The results show that there is a strong correlation between climate factors. ST, AT, DT, PRE, RH, and ETa are the main factors that have the potential to cause heavy rain, thunderstorms, and other severe weather. Meanwhile, PRE, RH, TRs, NRs, UVI, and SD are among the major factors linked to climate change. Specifically, SP, ST, AT, and WS are among the minor factors in most areas. The top ten provinces in terms of combined factor scores are Heilongjiang, Neimenggu, Qinghai, Beijing, Shandong, Xizang, Shanxi, Tianjin, Guangdong, and Henan. The trend of climate factors in China is expected to remain relatively stable over the next 30years, with a noteworthy decrease observed in CAPE compared to the past 71years. Our findings can help to better mitigate the risks associated with climate change and enhance resilience; they also provide a scientific basis for environmental, ecological, and agricultural systems to cope with climate change.

Combination Prediction of Income Gap between Urban and Rural Residents in China Based on IOWA Operator

Aims: Based on the income data of urban and rural residents in China from 1998 to 2021, the income gap variables of urban and rural residents were constructed, and the combination prediction method was used to predict the income gap between urban and rural residents in China.
 Methodology: Grey prediction model GM (1,1), Holt-winter seasonless exponential smoothing model and autoregressive moving average ARIMA model were used to construct an order weighted arithmetic mean combination model induced by IOWA with the minimum sum of error squares. Then, by building new weights, three individual forecasting models and combination forecasting models are used to forecast the income gap between urban and rural residents in the next five years.
 Results: The results show that the accuracy of the combined prediction model is significantly better than that of the single prediction model.
 Conclusion: It can be known that the income gap between urban and rural residents will widen further in the future, with an average growth rate of 4.55%.

A Novel Grey Seasonal Model for Natural Gas Production Forecasting

To accurately predict the time series of energy data, an optimized Hausdorff fractional grey seasonal model was proposed based on the complex characteristics of seasonal fluctuations and local random oscillations of seasonal energy data. This paper used a new seasonal index to eliminate the seasonal variation of the data and weaken the local random fluctuations. Furthermore, the Hausdorff fractional accumulation operator was introduced into the traditional grey prediction model to improve the weight of new information, and the particle swarm optimization algorithm was used to find the nonlinear parameters of the model. In order to verify the reliability of the new model in energy forecasting, the new model was applied to two different energy types, hydropower and wind power. The experimental results indicated that the model can effectively predict quarterly time series of energy data. Based on this, we used China’s quarterly natural gas production data from 2015 to 2021 as samples to forecast those for 2022–2024. In addition, we also compared the proposed model with the traditional statistical models and the grey seasonal models. The comparison results showed that the new model had obvious advantages in predicting quarterly data of natural gas production, and the accurate prediction results can provide a reference for natural gas resource allocation.

Forecasting education expenditure with a generalized conformable fractional-order nonlinear grey system model

As an important human capital investment, education is an effective means to improve the comprehensive quality of people. Education expenditure is an important material guarantee for the development of educational undertakings. Education expenditure data is highly susceptible to numerous economic and social factors that complicate its nonlinear structure. In order to model the complex nonlinear problems of the system, this paper proposes a generalized conformable fractional-order nonlinear grey prediction model for the first time by analyzing the traditional time series-based modeling method in a nonlinear grey domain. The proposed model expands on the classical grey Bernoulli model by introducing the generalized conformable fractional accumulation as a new accumulation generator and utilizes error minimization principles in the modeling process. By altering the optimal order of the model and the cumulative generation operator, this model can adapt to various time series and reduce errors. Finally, the model is applied to education expenditure forecasting, and it is proved that the proposed model achieved good results and has higher accuracy than other models.

A Study on the Optimal Investment Strategy Portfolio of Gold and Bitcoin Assets Based on Grey Prediction and Programming Models

A Study on the Optimal Investment Strategy Portfolio of Gold and Bitcoin Assets Based on Grey Prediction and Programming Models

A novel data-driven seasonal multivariable grey model for seasonal time series forecasting

Rapid growth in solar power generation, accompanied by random fluctuations, has important implications for the security, stability, and productivity of the grid. Therefore, accurate predictions of solar power generation provide an essential benchmark for relevant institutions to conduct grid planning and power dispatch. Thereby, a novel data-driven seasonal multivariable grey model is proposed for simulating seasonal fluctuations and non-linear trends in solar power generation. To be specific, the novel model contains the following refinements: first, the seasonal factors and time-power item are introduced to modify the insensitivity of traditional multivariable grey models to seasonal fluctuations and non-linear trends. Second, to enhance the generalizability and adaptability of the model, the parameter associated with the time-power item is determined by using the genetic algorithm. Third, on the premise of proving the model’s requirements for period length and sample size, the derivation of the time response function is expounded. For illustration and verification purposes, comprehensive experimental studies are conducted with quarterly and monthly time series samples. In addition, forecasts of the novel model are compared with five prevailing models, including grey prediction models, traditional econometric technology, and artificial intelligence. Case studies indicate that this novel model exhibits improved generalizability, stability, and reliability in the face of quarterly or monthly time series data, confirming it as an auspicious and powerful tool for future solar power generation forecasting.

Fractional-Order Accumulative Generation with Discrete Convolution Transformation

A new fractional accumulation technique based on discrete sequence convolution transform was developed. The accumulation system, whose unit impulse response is the accumulation convolution sequence, was constructed; then, the order was extended to fractional orders. The fractional accumulative convolution grey forecasting model GMr*(1,1) was established on the sequence convolution. From the viewpoint of sequence convolution, we can better understand the mechanism of accumulative generation. Real cases were used to verify the validity and effectiveness of the fractional accumulative convolution method.

Prediction method of building inclination around foundation pit based on Grey Model

In order to improve the prediction accuracy and effect of building inclination around foundation pit, a prediction method of building inclination around foundation pit based on grey model is designed in this paper. When calculating the parameters of the prediction model, the number of the bottom layer of the foundation pit excavation, the stress change value and the excavation load value are taken as the basis. At the same time, the grey derivative defined by the smooth discrete function is combined with the grey differential equation to build the grey model. The determined parameters are input into the grey prediction model to output the slope value of the buildings affected by the foundation pit. Complete the design of building inclination prediction method around the foundation pit. The experimental results show that the proposed method can improve the prediction of building inclination around the foundation pit, and clarify the direct relationship between foundation pit and building inclination, which verifies the effectiveness of the proposed method.

IMPLEMENTASI GREY MODEL (1,N) UNTUK SISTEM PERAMALAN JUMLAH TANGKAPAN IKAN

The increasing need for fish causes problems related to number of fish catches in the fisheries sector. In fish catches amount, all information related to fishing ground is well known, but on the other hand it is not easy to predict the number of fish catches due to unclear information. This is also related to the number of ships that make trips, the length (time) of the trip, the type of fishing gear, weather conditions, the quality of human resources, natural environmental factors, and others. The purpose of this study is to apply grey forecasting model GM (1.N) to forecast the number of fish catches. Grey forecasting models are used to build forecast models with limited amounts of data with short-term forecasts that will produce accurate forecasts. This study employs the data on monthly number of fish catches and wave height in the year of 2016 to 2018 to analyze calculations using the GM (1.N) models. The study was conducted with 36 time series data. The result showed that the MAPE on the GM (1.N) model of 57% in the experiment with 36 data.

Study on the three-child policy of the Chinese population based on the grey prediction model

In order to ensure the sustainable development of China's population and the stability of the economy and society, the implementation of the three-child policy [1] can solve the problem of China's population aging and promote the optimization of the population structure. Based on the analysis of the relevant population data in the National Bureau of Statistics, this paper mainly studies the problem of the three-child policy on China's population, and analyzes the changes of China's population. First, through the National Bureau of Statistics collected the data of China's population number, birth rate, mortality rate and natural growth rate in China in the past two decades, the change trend of China's population from 2021 to 2035 was obtained, and the final prediction results were obtained by using the grey prediction model GM (1,1). Secondly, the influence vector of the three-child policy is obtained by using the influence vector under the two-child policy, and the natural growth rate under the influence of the three-child policy from 2021-2025 is obtained. Then, the remaining natural growth rate from 2026-2035 is predicted according to the gray prediction model, and the predicted natural growth rate from 2022-2035 is deduced to deduce the population of China from 2022-2035. Finally, the study of this problem is related to China's population structure, labor resources, social security and economic development and other aspects, which needs to conduct in-depth research and analysis from multiple dimensions to formulate scientific and reasonable policies and measures, and provide important information for social and economic development planning.