Grey Prediction Model Research Articles

Ethiopian energy consumption forecast

This Energy consumption forecast is vital and has a great economic impact. Mathematical models developed for energy forecast can also serve as inputs for further studies. This study is intended to develop an energy consumption forecast using the grey prediction model GM (1,1), based on the actual energy consumption data from the year 2008 to 2017. The models are developed for the total, solid biomass, oil products, and electrical energy consumption; and the accuracy for each model is ratified. These developed forecasting models were used to anticipate six-year Ethiopian consumption of major energy types. The outcomes of models for all four energy consumption types show an upward trend; simulating and forecasting are found suited with the grey system model with development coefficient values less than 0.3 for all selected energy forms.

A novel multivariate grey model of energy consumption under carbon trading background and its application

Climate warming and the energy crisis have been high-profile issues today, and carbon trading is considered one of the essential means to solve the problem. Carbon trading cannot ignore the impact on energy consumption and carbon emissions. Therefore, it is beneficial to develop a low-carbon economy to scientifically predict the trend of energy consumption and make timely investment decisions and policies in carbon trading. Based on the modelling mechanism of energy economy and the grey forecasting model, this paper studies the dynamic relationship among energy consumption, carbon emissions, economic growth, and carbon trading price and then constructs a grey prediction model of energy consumption driven by carbon trading. The least-square method and recursive mathematical method were used to obtain the model’s parameter estimation and corresponding time formula, respectively. In the end, we apply the model to the energy consumption in China, and three experimental results reveal that the prediction performance of the novel model is significantly better than the existing four grey models. And it is predicted that the energy consumption of coal in China will increase by 15.3118% in 2025, which can provide an essential reference for the energy structure adjustment and environmental policymaking in China.

An Intelligent Gray Prediction Model Based on Fuzzy Theory

In order to improve the forecasting effect of the gray prediction model, this paper combines the fuzzy theory to construct the gray prediction model and explores its forecasting accuracy. Moreover, this paper uses the entropy weight method to obtain the objective weight to correct the subjective weight, which makes the weight calculation more reasonable. In view of the uncertainty of the control signal of the research object, this paper introduces the gray system theory to conduct cluster analysis on the fire control computer and mainly introduces the general whitening weight function. Furthermore, this paper adopts the center point mixed with a triangular whitening weight function to carry out gray clustering according to the difficulty of defining the gray class boundary and gives the solution steps to obtain the intelligent gray prediction model. Finally, this paper verifies that the intelligent gray prediction model based on fuzzy theory has a good effect through experiments, which can effectively improve the prediction effect of the intelligent prediction model.

A Study of Forest Carbon Sink Increment from the Perspective of Efficiency Evaluation Based on an Inverse DEA Model

Forest carbon sink efficiency refers to the efficiency of input-output indicators related to carbon sinks. This paper studies carbon sink efficiency from the perspective of resource allocation; guides the optimal allocation of resources; and selects forestry employees, forestry investment amount and afforestation area as input indicators; the forest carbon sink efficiency in China is calculated and analyzed based on a data envelopment analysis model by converting the forest volume into the forest carbon sink through the volume expansion factor method. The grey prediction model is used to estimate the change in the input indicator, and the production possibility set is constructed with the input indicator before and after the change and the current output indicator. The efficiency of the decision units before the change is calculated, and through the comparison of efficiency, the conditions of forest carbon sink increase in 15 provinces are obtained. The optimal allocation of the output indicator is calculated based on the inverse data envelopment analysis model. The largest increase in forestry carbon sink is 169,362 megatons in Guangdong, and the smallest is 619 megatons in Tianjin. Finally, some suggestions for the path of forest carbon sink increment are put forward.

Optimized multivariate grey forecasting model for predicting electricity consumption: A China study

The power industry has significantly contributed to the prosperity of the national economy, and accurate prediction can reflect the development trend of the power system and power market. The short-term electricity consumption of a country exhibits both annual growth certainty and random change uncertainty, which can be suitably considered with the grey forecasting model. Regarding the short-term trends of electricity consumption in China, this study established an optimized multivariate grey forecasting model with variable background values (OGM(1, N) model) to forecast the electricity consumption level in China. The established model could be converted into the GM(1, N) model and different variant models by adjusting the model parameters. With Beijing, Tianjin and Shanghai as examples, the OGM(1, N) model is compared to the GM(1, N) model and its variant model. The excellent prediction results confirm the feasibility of the proposed model. Then, the proposed model is applied to study China’s electricity consumption. The research results indicated that the OGM(1, N) model attains an extraordinarily high precision in the prediction of electricity consumption and can provide a practical reference for accurate electricity consumption prediction.

Modeling, prediction and analysis of new energy vehicle sales in China using a variable-structure grey model

At present, the new energy vehicle (NEV) industry in China is at a huge risk of overheated investment and overcapacity. An accurate prediction of China’s future NEV market is of great significance for the Chinese government to control the growth of the industry at a reasonable speed and the production on a reasonable scale. To this end, a new grey prediction model with a variable structure was established considering the data characteristic of small sample size of China’s NEV sales. In the new model, the value range and optimization space of the order r were expanded, and the definitions and structures of two operators, the grey accumulating operator and grey inverse operator, were unified. Meanwhile, the new model had good structural variability and was fully compatible with other grey models of the same type. The performance of the model was tested with different data sequences, and results showed that the comprehensive performance of this model was better than that of other similar models. Lastly, the model was employed to predict China’s NEV sales. Results showed that the sales were expected to be 3.03 million in 2030, which indicates that China’s NEV market will continue to grow, but at a significantly slower rate. The government and enterprises need to take corresponding measures to promote the healthy development of China’s NEV industry.

Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China

In this paper, GM(1,Nr) model is established to improve the traditional GM(1,N) model from three aspects: (1) transforming the original sequence to satisfy the modeling conditions with particle swarm optimization algorithm; (2) introducing grey incidence analysis to obtain the grey incidence ranking and carrying out stepwise test for significant variables to determine the number of variables; and (3) predicting the related factor sequence through the improved GM(1,1) model. Empirical analysis shows that the proposed GM(1,Nr) model has remarkable good prediction performance compared with the traditional grey forecasting model. It is also demonstrated that the extraction of influencing factors can significantly improve the prediction effectiveness, especially when pursuing the best fitting effect on small sample data. The findings indicate that the electricity consumptions of Jiangsu Province in the next several years will be at a high level and keep rising, with a predicted value of 9712.48 billion kilowatt-hours in 2030. The findings can help the government and energy related institutions to develop management policies on energy demand, and the proposed model can also be extended for the application in other regions.

Tourism combination forecasting using a dynamic weighting strategy with change-point analysis

ABSTRACT Combination forecasting is an important in the literature on tourism. This study considers three important issues to develop a more accurate combination forecasting method for tourism forecasting. We address the unrealistic requirement related to the statistical properties of the collected data for model fitting, assign changing rather than fixed weights to single models, and incorporate nonlinear relationships among single-model forecasts into forecast combinations. This leads us to develop a three-stage procedure for combination forecasting that consists of generating single-model forecasts with grey prediction models, detecting significant changes in the time series to determine when to update the weights for combining the forecasts, and nonlinearly combining individual forecasts based on a dynamic weighting strategy. In contrast with commonly used fixed weighting, the dynamic weighting used here involves the use of change points to identify the period for which the weights need to be re-estimated. The inbound demand for tourism in Taiwan was used as an empirical case to assess the performance of the proposed framework for forecast combinations. The results show that the nonlinear fuzzy integral with the proposed dynamic weighting strategy significantly outperforms that with the fixed weighting strategy, and has a superior forecasting accuracy than other combined models.

Grey Markov prediction-based hierarchical model predictive control energy management for fuel cell/battery hybrid unmanned aerial vehicles

The energy management problem of hybrid unmanned aerial vehicles (UAVs) is studied in this paper, and an energy management strategy based on hierarchical model predictive control (HMPC) is proposed. The structure of HMPC is divided into the trajectory optimization layer and the control layer. The trajectory optimization layer primarily considers the factors like economic costs, including hydrogen consumption, equipment purchase, use costs, and equipment lifetime. To determine the optimal trajectory of the battery state of charge, the trajectory optimization layer is optimized and solved. The control layer is model predictive control, and its key function is to follow the reference trajectory to obtain the optimal fuel cell output power. A grey Markov prediction model is proposed and used to predict the future power demand of UAVs. The superiority of the prediction model is demonstrated by comparing it with the typical prediction methods. Based on the simulation and experimental comparison, it can be concluded that the effect of the HMPC is satisfactory and has a positive impact on the endurance of the UAV.

Prediction of Translation Ability of Complex English Sentences Based on Artificial Neural Network

The current translation training programs at colleges and universities are not fully adequate for students majoring in English, and some students do not perform well in their execution of translation tasks. Translation ability is a frontier topic in the field of translation. A number of studies have provided some new ideas for research on the main goals of translation education and the evaluation of students’ translation abilities, but few has used artificial intelligence to predict students’ translation abilities. This paper analyzes the prediction of the translation ability with respect to complex English sentences. First, based on the principles of accuracy, operability and scientificity, an evaluation indicator system was built to fully reflect the basic characteristics of students’ ability to translate complex English sentences, which is in line with the training needs of translation talents at colleges and universities. Then, based on the characteristics of the logical structures of complex English sentences, a grey Bernoulli prediction model was constructed for the translation ability of complex English sentences. The particle swarm optimization algorithm was selected as the parameter optimization algorithm for the prediction model of translation ability with respect to complex English sentences, with the optimization targets being the Bernoulli parameters and the order of the fractional-order accumulation generation. The experimental results verified the effectiveness of the proposed model.

Research on construction deformation prediction and disaster warning of karst slope based on mutation theory

In the study of deformation prediction and disaster warning during karst slope construction, the influencing factors and deformation law should be comprehensively considered. The layout of the deformation monitoring points of karst slope is affected by the thickness of karst overburden soil, dissolution and fragmentation degree, karst development degree, slope cracking degree, fault or weak interlayer and other factors. In this paper, the author aimed at the problem of construction deformation prediction and disaster warning of karst slope, proposed an improved model of cusp mutation by applying and optimizing the cusp mutation model, analysed the deformation trend and sudden change type of the slope, and obtained the critical control early warning value of slope deformation. Therefore, it is feasible to analyse the deformation and mutation characteristics of karstified slope by using a virtual reality-mutation model. In addition, based on the empirical formula of the slope sliding limit deformation rate and grey prediction model, the critical control warning value of slope deformation is obtained, which provides a basis to quantify the deformation index of risk evaluation. This method provides a new idea to predict karst slope construction deformation and catastrophic deformation warning and has a reference value for similar engineering examples.

Research on Spatio-Temporal Pattern Evolution and the Coupling Coordination Relationship of Land-Use Benefit from a Low-Carbon Perspective: A Case Study of Fujian Province

To accelerate the ecological civilization in the new era and coordinate the region’s ecological economic growth, land-use benefit pre-evaluations and coordination analyses must be carried out. These should be based on a land-use structure layout that is low-carbon oriented to ensure the land is utilized efficiently and sustainably. This paper studies Fujian Province, the national ecological civilization test area, and constructs a framework for land-use benefit evaluation and coupling coordination relationship analysis based on land-use structure from the low-carbon perspective. Based on land-use status data from 2000 to 2020, land-use simulation data under two development scenarios in 2030, and accounting for the different types of main functional areas, this paper establishes the land-use benefit evaluation model. The model uses aspects of carbon emission benefit, economic benefit, and ecological benefit to calculate the land-use benefit of counties (cities and districts) in Fujian Province from 2000 to 2030 by combining with the grey prediction model. The coupling coordination degree model is used to explore the coordination relationship between the above three types of benefits in different districts and counties in 2030 and to identify the main factors restricting the improvement of coupling coordination relationships in different regions in the future. The following results are found: (1) From the perspective of land-use efficiency, the coupling and coordination of land-use efficiency and benefits of counties (cities and districts) in Fujian Province continued to rise from 2000 to 2030 under the low-carbon development scenario, and the spatial agglomeration characteristics were obvious. By 2030, the coupling and coordination relationship of regional land-use efficiency was gradually optimized, and the development pattern with the Fuzhou metropolitan area and Xiamen-Zhangquan metropolitan area as the core was formed. (2) From the perspective of restrictive factors of land-use efficiency coupling coordination, the improvement of land-use efficiency coupling coordination relationship in each county (city and district) is affected by multiple factors. The influence degree is economic benefit > ecological benefit > carbon emissions. These results will help to promote the efficient and sustainable use of land resources and realize the comprehensive and coordinated development of a low-carbon economy and society.

Study on spatial-temporal characteristics and influencing factors of urban environmental resource efficiency in the Yangtze River Basin of China

There is a close relationship between environmental resource efficiency and the high-quality development of the river basin economy. Improving urban environmental resource efficiency is of great significance to the high-quality development of the river basin. Based on the environmental panel data of cities in the Yangtze River Basin from 2004 to 2020, this paper applies the DEA-Malmquist index model to explore the static characteristics and dynamic changes in the overall environmental resource efficiency of cities in the Yangtze River Basin. Then, the spatial and temporal aspects of the environment are discussed by combining the kernel density function method. The Tobit regression model is used to analyze the factors affecting the environmental resource efficiency of cities in the Yangtze River Basin and its importance. Finally, the grey prediction model is utilized to predict the undesirable output data of cities in the Yangtze River Basin from 2023 to 2025. The results show that the overall urban environmental resource efficiency level in the Yangtze River Basin is high. However, the number of cities that achieve DEA efficiency is less than that of non-DEA efficient cities. The Total Factor Productivity (TFP) of cities shows a “high-low-high” trend, and the technical efficiency change (Effch) and pure technical efficiency change (Pech) have an improvement trend. Industrial structure, regional factors, and openness are positively correlated with environmental resource efficiency, during the economic scale and environmental governance level negatively moderate environmental resource efficiency. The forecast results show that the undesired output of cities in the Yangtze River Basin has been somewhat controlled.

Prediction of the Total Output Value of China’s Construction Industry Based on FGM (1,1) Model

The total output value of the construction industry (TOVCI) reflects its own development level to a certain extent. An accurate prediction of the construction industry’s total output value is beneficial to the government’s dynamic regulation. The grey prediction model is widely used for its simple calculation process and high prediction accuracy. Based on the TOVCI of China from 2017 to 2020, this paper constructs an FGM (1,1) model, calculates r by a simulated annealing algorithm, and forecasts the TOVCI of China in next few years. At present, the Particle Swarm Optimization algorithm (PSO) is employed in the calculation of r in the literature. However, the advantage of the simulated annealing algorithm is its powerful global search performance. The prediction results indicate that the TOVCI of China will continue to grow, but the growth rate will slow down. Therefore, the construction industry of China should not simply pursue the high-speed growth of the total output value, but pay more attention to high-quality development, such as technological innovation, energy conservation and environmental protection. Finally, the limitations and future research directions are elucidated.

Forecasting of non-renewable and renewable energy production in India using optimized discrete grey model.

Renewable energy delivers reliable power supplies and fuel diversification, enhancing energy security and lowering fuel spill risk. Renewable energy also helps conserve the nation's natural resources. Solar and other renewable energy sources have become increasingly prominent in recent years. India has achieved the 20 GW capacity solar energy production target before 2022. It is presently producing the lowest-cost solar power at the global level. Thermal energy has dominated the energy market. Countries have decided on energy generation from renewable sources and adopting green energy. This study forecasted non-renewable and renewable energy from multiple sources (hydropower, solar, wind and bioenergy) using grey forecasting model DGM (1,1,α). The comparative analyses with the classical models DGM (1,1) and EGM (1,1) revealed the superiority of the DGM (1,1,α). We also used CAGR for 2009-2019 to compare the actual and predicted data growth rate. The results show that non-renewable and renewable energy production is expected to increase. However, renewable energy generation wind sources continue to increase faster than hydropower, solar and bioenergy.

NLOS Identification- and Correction-Focused Fusion of UWB and LiDAR-SLAM Based on Factor Graph Optimization for High-Precision Positioning with Reduced Drift

In this study, we propose a tightly coupled integrated method of ultrawideband (UWB) and light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) for global navigation satellite system (GNSS)-denied environments to achieve high-precision positioning with reduced drift. Specifically, we focus on non-line-of-sight (NLOS) identification and correction. In previous work, we utilized laser point cloud maps to identify and exclude NLOS measurements in real time to attenuate their severe effects on the integrated system. However, the complete exclusion of NLOS measurements will likely lead to deterioration in the dilution of precision (DOP) for the remaining line-of-sight (LOS) anchors, counterproductively introducing large positioning errors into the integrated system. Therefore, this study considers the ranging accuracy and geometric distribution of UWB anchors and innovatively proposes an NLOS correction method using a grey prediction model. For a poor line-of-sight (LOS) anchor geometric distribution, the grey prediction model is used to fill in the gaps by predicting the NLOS measurements based on historical measurements. Including the corrected measurements effectively improves the original poor geometric configuration, improving the system positioning accuracy. Since conventional filtering-based fusion methods are exceedingly sensitive to measurement outliers, we use state-of-the-art factor graph optimization (FGO) to tightly integrate the UWB measurements (LOS and corrected measurements) with LiDAR-SLAM. The temporal correlation between measurements and the redundant system measurements effectively enhance the robustness of the integrated system. Experimental results show that the tightly coupled integrated method combining NLOS correction and FGO improves the positioning accuracy under a poor geometric distribution, increases the system availability, and achieves better positioning than filtering-based fusion methods with a root-mean-square error of 0.086 m in the plane direction, achieving subdecimeter indoor high-precision positioning.

Novel Fractional Grey Prediction Model with the Change-Point Detection for Overseas Talent Mobility Prediction

Overseas students constitute the paramount talent resource for China, and, hence, overseas talent mobility prediction is crucial for the formulation of China’s talent strategy. This study proposes a new model for predicting the number of students studying abroad and returning students, based on the grey system theory, owing to the limited data and uncertainty of the influencing factors. The proposed model introduces change-point detection to determine the number of modeling time points, based on the fractional-order grey prediction model. We employed a change-point detection method to find the change points for determining the model length, based on the principle of new information priority, and used a fractional order accumulated generating operation to construct a grey prediction model. The two real data sets, the annual number of students studying abroad and returning students, were employed to verify the superiority of the proposed model. The results showed that the proposed model outperformed other benchmark models. Furthermore, the proposed model has been employed to predict the tendencies of overseas talent mobility in China by 2025. Further, certain policy recommendations for China’s talent strategy development have been proposed, based on the prediction results.

Grey system theory in sustainable development research—a literature review (2011−2021)

PurposeAs the grey systems theory has been widely used in the field of sustainable development (SD) research, in the following, a short literature overview will be put forward, starting from the usage of these theories in the economic development, social inclusion and environmental protection contributions to the evolving process of SD during 2011–2021. The purpose of this paper is to identify some key studies from all the SD areas in which the grey systems can be used in order to open and to bring the researchers to new domains in which they can reveal their interest and in which they can successfully use the methods offered by the grey systems theory.Design/methodology/approachUsing the search engine offered by the Google Scholar and the Web of Science (WoS), a literature review has been performed for the grey systems applications on SD research on both grey relational analysis (GRA) and grey forecasting. In addition, some grey evaluation theories – clustering evaluation models and grey target decision models – have also been presented.FindingsMany grey models are widely used in the field of SD. Compared with other methods such as grey prediction, grey evaluation and decision-making model, GRA technology is the most used method, and the research using this method is more than three times that of all other methods.Research limitations/implicationsThe present paper identifies some of the most representative examples in which the grey system theory (GST) has been used, but, in the same time, there are a lot of studies that have not been mentioned here due to the lack of space.Originality/valueThe present paper focuses on the SD applications in which GST has been successfully used, bringing to the reader a general overview on this field and, in the same time, enables new research perspectives.

A novel multivariate grey model for forecasting periodic oscillation time series

To solve the problem that the grey multivariate prediction model cannot well simulate systems with periodic oscillations, a novel multivariate grey model called the GM(1,N|sin) power model is proposed. The power exponential term and dynamic sinusoidal function are developed to represent the nonlinear relationship and periodic oscillations of the independent and dependent variables in the proposed model, respectively. First, the discrete time response formula for generating the simulated values at each time point is given. Second, the nonlinear programming model based on the particle swarm optimization algorithm is established to solve the power exponential and periodic coefficients. In addition, to enhance the generalization ability of the parameters, an improved nonlinear programming model considering the accuracy of the validation set is constructed. Finally, in the case studies, the quarterly electricity consumption of Jiangsu Province and PM2.5 concentrations in Nanjing are adopted to test the effectiveness of this model, and the results are obtained respectively through the GM(1,N|sin) power model and alternative models. The results indicate that the accuracy of GM(1,N|sin) power model in the time series with periodic oscillations outperforms the other six models in this paper.

Application of Grey Deformation Prediction Model Optimized by Double Coefficient for Tailings Dam

As a nonuniform structure, tailings dam undergoes complex and chaotic nonlinear changes, under the joint influence of multiple dynamic or nondynamic factors. These changes make it difficult to predict the deformation of tailings dam accurately with a numerical model. To solve the problem, this paper proposes a grey deformation prediction model optimized by double coefficient (GDPM-DC). Unlike a single grey prediction model, the GDPM-DC does not mutate significantly but adapts well to specific scenarios. Besides, the model can smoothen and stabilize the original data and thus achieve accurate prediction of the deformation of tailings dam. The main results are as follows. The GDPM-DC made more accurate predictions than the traditional grey model (1, 1) (GM (1, 1)), the grey model based on logarithmic transformation (GM-LT), and the grey model optimized by weight coefficient (GM-WC). It significantly improved the overall prediction accuracy of the vertical and transverse deformations of the dam and controlled the relative error of the predicted seepage pressure to 2.79%–3.71%. Moreover, the model could forecast the trend component and random fluctuation component of seepage pressure effectively, fit the increasing trend in stages 1–3 and the decreasing trend in stages 3–9, and realize the quantitative prediction of deformation law for the operating tailings dam. The research results provide a meaningful reference for the instability analysis and safety management of tailings dam.