Grey Prediction Model Research Articles

Optimizing Remaining Useful Life Estimation of Lithium-Ion Batteries: A Particle Swarm Optimization-Based Grey Prediction Model

Accurate estimation of the age and condition of lithium-ion batteries (LIBs) is paramount for their safe and economically viable utilization. However, assessing the degradation of these power units proves challenging due to their dependence on various environmental and usage factors. In this study, we propose an efficient Particle Swarm Optimization (PSO)-based Grey Theory prediction model to determine the Remaining Useful Life (RUL) of lithium-ion batteries.
 The proposed model utilizes PSO to optimize the coefficients of a grey prediction model, enabling accurate forecasting of the remaining useful life of LIBs. Our results demonstrate that the presented model outperforms conventional grey prediction models in terms of both accuracy and stability. Furthermore, the proposed model offers simpler predictions compared to existing models in the literature.
 By introducing this promising technique, our study contributes to the precise forecasting of the RUL of lithium-ion batteries and holds potential for applications in similar domains. This research serves as a significant step towards ensuring effective management and utilization of LIBs, promoting their reliability and safety.

A novel fractional order variable structure multivariable grey prediction model with optimal differential background-value coefficients and its performance comparison analysis

PurposeTraditional multivariable grey prediction models define the background-value coefficients of the dependent and independent variables uniformly, ignoring the differences between their physical properties, which in turn affects the stability and reliability of the model performance.Design/methodology/approachA novel multivariable grey prediction model is constructed with different background-value coefficients of the dependent and independent variables, and a one-to-one correspondence between the variables and the background-value coefficients to improve the smoothing effect of the background-value coefficients on the sequences. Furthermore, the fractional order accumulating operator is introduced to the new model weaken the randomness of the raw sequence. The particle swarm optimization (PSO) algorithm is used to optimize the background-value coefficients and the order of the model to improve model performance.FindingsThe new model structure has good variability and compatibility, which can achieve compatibility with current mainstream grey prediction models. The performance of the new model is compared and analyzed with three typical cases, and the results show that the new model outperforms the other two similar grey prediction models.Originality/valueThis study has positive implications for enriching the method system of multivariable grey prediction model.

Exploring the Carbon Footprint of Energy Consumption in Shanxi Province Based on the Tapio Decoupling and Grey BP Neural Network Model

This paper focuses on analyzing the carbon footprint of energy consumption in Shanxi Province, China. It utilizes data from sources such as the 'China Energy Statistical Yearbook' (2001-2022), the China Carbon Accounting Database, and the 'Shanxi Statistical Yearbook' for an exploratory analysis of energy consumption and the province's economy. The study calculates the carbon footprint of energy consumption in Shanxi Province and employs the LMDI model to identify influencing factors. Additionally, it uses the Tapio decoupling model to assess the relationship between these factors and economic development. To forecast the carbon footprint from 2022 to 2026, a grey BP neural network prediction model is applied, demonstrating improved accuracy compared to BP and grey models, with MAE and RMSE values of 2.44 and 10.91.

Research on pricing and replenishment strategy of superstore goods based on linear regression and gray prediction models

The pricing and replenishment strategy of various commodities in superstores have a significant impact on the revenue. This paper aims to address the automatic decision-making problem for pricing and replenishment of vegetable commodities in superstores. It applies the linear regression model to predict commodity prices, aiming to maximize the revenue of superstores. Additionally, it utilizes the gray prediction model to forecast replenishment volume. Based on marketing data from 2020 to 2023 for vegetable goods at a specific superstore, this study provides the replenishment quantity and pricing strategy for each vegetable category from July 1-7, 2023, as well as for each individual vegetable item on July 1, 2023.

A novel structure adaptive discrete grey Bernoulli prediction model and its applications in energy consumption and production

It is well known that energy forecasts play an important role in guiding energy policy, economic development and technological progress. Therefore, based on the purpose of energy consumption and production forecasting, this paper proposes a novel structure adaptive discrete grey Bernoulli model, which is innovative in terms of both accumulated generating operator and model structure. In terms of accumulated generating operator, a new fractional order accumulated generating operator is proposed in this paper. The new accumulated generating operator has a different information priority by adjusting the values of the parameters. In terms of model structure, a novel discrete grey Bernoulli model is proposed in this paper. The novel model is well adapted to time series data containing nonlinear information, and can well mine and utilize the information contained in the original data. In addition, the Particle Swarm Optimization (PSO) algorithm was chosen to optimize the model parameters based on algorithm comparison experiments. This enables the model to flexibly adapt to a variety of complex data and has the ability of structure adaptive. Moreover, this paper conducts comparative experiments between the novel model and eight other forecasting algorithms for time series data. The numerical results show that the novel model has better forecasting performance for the data of China’s total energy consumption, China’s total electricity generation and China’s total domestic electricity consumption. In addition, for the model reliability problem caused by the optimization algorithm, the stability and accuracy of the model are verified by Monte Carlo simulation and probability density visualization analysis. Finally, the proposed model predicts the future development trend of energy consumption and production in China.

Grey Duane model for reliability growth prediction under small sample uncertain failure data

Traditional reliability growth models require failure data to adhere to a specific distribution, which greatly limits the applicability in data-scarce scenarios. Drawing from the characteristic in modeling small sample and poor information of grey system theory, this study introduces a novel grey Duane reliability growth prediction model, tailored for analyzing uncertainty in limited failure data. The characteristics and adaptability of the proposed grey Duane model (GDM) are analyzed and compared with two traditional reliability growth prediction models. Utilizing a first-order accumulation generation operation, a differential function is established to estimate the unknown parameters in GDM. The proposed GDM enables synchronized predictions of failure time, failure number, and instantaneous mean time between failures. To validate the applicability of GDM in reliability growth management, an industrial case in particular electronic equipment during an aircraft’s flight-testing process is applied, whose results demonstrate its superior predictive accuracy compared to alternative models.

Experimental study on the influence of water-rock interaction on the time-varying characteristics of coal rock mass.

In order to study the influence of water-rock interaction on the mass time-varying characteristics of coal rocks, coal was selected as the research object and subjected to chemical immersion tests with different pH aqueous solutions for 12days. By experiment, the time-varying patterns of mass change fraction in coal samples, pH value in solution, and ions concentration of calcium and magnesium were obtained. Based on the gray correlation theory, the correlation degree between the mass change fraction and four influencing factors was analyzed. The gray prediction models for the mass time-varying characteristics of coal rocks have been established. The research shows that: (1) the influence waysanddegree of different pH aqueous solutions on the mass changes of coal rocks are different, (2) during the process of water-rock interaction, the change law of pH value, ions concentration of calcium and magnesium in solution are obvious, (3) the multiple regression modelscan be used to predict the mass change of coal rocks accurately under water-rock interaction.

Seasonal electricity consumption forecasting: an approach with novel weakening buffer operator and fractional order accumulation grey model

PurposeAccurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system. Consequently, it fosters reasonable scheduling plans, ensuring the safety of the system and improving the economic dispatching efficiency of the power system.Design/methodology/approachFirst, a new seasonal grey buffer operator in the longitudinal and transverse dimensional perspectives is designed. Then, a new seasonal grey modeling approach that integrates the new operator, full real domain fractional order accumulation generation technique, grey prediction modeling tool and fruit fly optimization algorithm is proposed. Moreover, the rationality, scientificity and superiority of the new approach are verified by designing 24 seasonal electricity consumption forecasting approaches, incorporating case study and amalgamating qualitative and quantitative research.FindingsCompared with other comparative models, the new approach has superior mean absolute percentage error and mean absolute error. Furthermore, the research results show that the new method provides a scientific and effective mathematical method for solving the seasonal trend power consumption forecasting modeling with impact disturbance.Originality/valueConsidering the development trend of longitudinal and transverse dimensions of seasonal data with impact disturbance and the differences in each stage, a new grey buffer operator is constructed, and a new seasonal grey modeling approach with multi-method fusion is proposed to solve the seasonal power consumption forecasting problem.HighlightsThe highlights of the paper are as follows:A new seasonal grey buffer operator is constructed.The impact of shock perturbations on seasonal data trends is effectively mitigated.A novel seasonal grey forecasting approach with multi-method fusion is proposed.Seasonal electricity consumption is successfully predicted by the novel approach.The way to adjust China's power system flexibility in the future is analyzed.

Pulse fractional grey model application in forecasting global carbon emission

Global climate problem has attracted attention from all over the world. How to correctly reflect greenhouse gas emission trend is not only an environmental problem, but also concerns human society's sustainable development. Energy consumption is the primary source of greenhouse gas emission. It is necessary to accurately forecasting energy consumption greenhouse gas emission future trend. COVID-19 epidemic has brought respite from global climate change problem by reducing energy consumption through home office, work stoppage, and global travel ban. However, in the post-epidemic period, energy consumption greenhouse gas emission intensity trend has become the center research field. This study takes global energy consumption carbon emission problem as the main research line and focuses on forecasting global energy consumption carbon dioxide emission trend in major regions based on COVID-19 epidemic shock background. The study considering epidemic shock volatility characteristic. Firstly, fractional order grey mode (FGM(1,1))is used as the baseline model to balance time series data weight. Secondly, Median absolute deviation data preprocessing is introduced to reduce data fluctuation. Finally, a novel delaying pulse shock function optimized background grey forecasting model is also proposed to reflect epidemic shock-response characteristic. The proposed model is compared with existing models. It is found that data preprocessing and novel proposed model not only improves historical data's fitting quality by reflecting COVID-19 epidemic's shock characteristic, but also showed excellent forecasting performance for future trend. The novel grey model largely solves existing model underfitting/overfitting problem. In the end, based on forecasted results, we summarize research conclusion and implication.

A fluctuation data grey model and its prediction of rainstorm days

Rainstorm is one of the most severe and frequent meteorological disasters. The rainstorm event brings considerable risks to the natural ecosystem and human life. There is a limited body of research on applying the grey prediction model for predicting rainstorm-related data. Based on the extreme precipitation index defined by the Expert Team on Climate Change Detection and Indices, this paper uses the data of rainstorm days from 493 weather stations in the middle and lower reaches of the Yangtze River to establish a prediction model. Improving the accuracy of rainstorm days prediction can bring valuable results to decision-makers in a specific region. The annual rainstorm days series exhibits randomness, irregular nonlinearity, fluctuations, etc.; however, the traditional grey prediction model can only identify the trend of a series but not its fluctuations, making its prediction extremely challenging. Therefore, this paper introduces a new dynamic grey action quantity based on the traditional grey model, and the parameters are solved by the hybrid technique of the genetic algorithm and the constrained fmincon function. The prediction results of the new model are compared with those of the traditional grey model, the seasonal grey model, the neural network, and the seasonal autoregressive integrated moving average. In terms of prediction accuracy and similarity of fluctuations, the results demonstrate that the new model outperforms these models.

An adaptive routing strategy in P2P-based Edge Cloud

P2P-based Edge Cloud (PEC) is widely used in Internet of Things (IoT). Inevitably, the sensor data routing technology has a significant impact on the performance of PEC. Due to its prevalence and complexity, the existing routing technologies in PEC need to be optimized. Specifically, key factors such as overall network traffic, user access latency, and resource utilization of edge nodes should be considered to adapt to the dynamic requirements of user request services and network topology. In order to address the challenges produced by these factors, an adaptive routing in P2P-based Edge Cloud is proposed, which is named ARPEC. In our approach, a target edge node selection scheme based on message activity and network topology is proposed, aiming to minimize the load on edge node and user access latency. Furthermore, to minimize system overhead, sensor data routing is mapped to minimum cost maximum flow (MCMF) graph. On this basis, a target edge node selection algorithm based on a grey linear regression combination prediction model is designed, and an incremental MCMF algorithm based on belief propagation (BP) is proposed. The evaluation results show that our approach can effectively improve PEC transmission performance and user experience.

Predicting China’s elderly population by using a fractional grey prediction model with time power term

Accurate prediction of the aging population can provide valuable reference and corresponding theoretical support for the adjustment of national population development policy and economic development strategy. To explore the future development trend of China’s aging population, this paper establishes a novel fractional grey prediction model with the time power term (abbreviated as FGM (1, 1, t α) model) to study China’s aging population. FGM (1, 1, t α) has the properties of fractional order accumulation operation and GM (1, 1, t α) model, which makes it good at capturing nonlinear features in time series. Furthermore, the quantum genetic algorithm is used to search for unknown parameters in the model to facilitate the solving task of the model. Data on China’s aging population from 2000 to 2009 are used to train the prediction models, and data from 2010 to 2019 are used to evaluate the models’ prediction performance. The results show that the FGM (1, 1, t α) model outperforms the other competing models, which means that it has good generalization. Finally, the FGM (1, 1, t α) model is used to forecast China’s aging population from 2020 to 2029.

Environmental performance evaluation of electric enterprises during a power crisis: Evidence from DEA methods and AI prediction algorithms

With the continuous occurrence of power crisis events worldwide, meeting society's demand for electricity has kept coal-fired power generation high, which leads to a large amount of carbon dioxide emissions and environmental pollution problems. Therefore, improving the environmental performance of thermal power plants under the background of a power crisis has become particularly important. This research provides a new path for production optimization and environmental performance improvement of electric power enterprises from the perspective of combining prediction algorithms and DEA methods. We apply the modified non-radial directional distance function (MNDDF) method to a set of panel data containing 16 different cogeneration units and 27 different periods in eastern China and construct the grey prediction model to predict the future environmental performance of these units. Empirical analysis shows that our model has high prediction accuracy and offers a reasonable basis for future production decision-making adjustments for electric power enterprises. Based on the results herein, we present valuable suggestions for thermal power plants to improve their environmental performance during a power market crisis, including optimizing resource utilization, improving management level and operating conditions, strengthening technological innovation, and enhancing production factor allocation.

Application of grey system model with intelligent parameters in predicting regional electricity consumption

PurposeGiven the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.Design/methodology/approachFirst, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.FindingsThe study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.Originality/valueAcknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

Optimization study of tourism total revenue prediction model based on the Grey Markov chain: a case study of Macau

<abstract> <p>The GM (1, 1) model, grounded in gray system theory, utilizes first-order cumulative data for forecasting. While offering simplicity and efficiency, its applicability is confined to such data. In light of the constraints inherent in the conventional gray GM (1, 1) prediction model when confronted with stochastic data fluctuations, the residual correction methodology was deployed to enhance the predictive efficacy of the GM (1, 1) model. Subsequently, an augmented model underwent refinement through the application of the Markov chain, giving rise to a sophisticated and optimized gray Markov chain prediction model. The efficacy of this novel model was substantiated through a case study involving the prediction of Macao's aggregate tourism revenue. A comparative analysis was conducted between the outcomes generated by the traditional gray prediction model, those of the refined prediction model, and the empirical data pertaining to tourism. This scrutiny validated the proficiency and precision of the optimized prediction model. The process of model optimization manifested a discernible enhancement in both predictive accuracy and stability, thereby broadening the prospective applications of gray prediction models. This endeavor aspired to furnish a scientifically grounded point of reference for the advancement of tourism within the Guangdong-Hong Kong-Macao Greater Bay Area and, indeed, throughout China. Moreover, it introduced a fresh methodology that held promise as a decision-making support mechanism for the developmental trajectory of Macao's tourism industry.</p> </abstract>

Research on the Application of Gray Prediction and BP Neural Network in English-Chinese Translation Quality Assessment

Abstract Gray prediction model and BP neural network are increasingly used in many fields as important prediction and analysis tools. The article first elaborates on the related algorithm of the gray prediction method. It constructs the gray prediction model, then constructs the BP neural network, and builds the gray BP neural network translation quality assessment model through the modeling of the simulation of the artificial neural network, and finally takes eight postgraduate students of translation majors of Beijing Foreign Studies University as the subjects, and the eight students translate two articles with similar word counts and difficulties, and score the translation quality of the subjects, and reasonably evaluate and scientifically predict these translations in order to validate the grey translation quality of subjects. The quality of translation is scored, and these translations are reasonably evaluated and scientifically predicted to verify the role and effect of the gray BP neural network in the quality of English-Chinese translation. The study concludes that the results based on the gray prediction model are that the prediction results are in the error range of 0%–7% compared with the original data, and the overall error is small. The prediction results of the compensatory fuzzy neural network prediction model for all 8 subjects before and after translation were more accurate than those predicted by the traditional fuzzy neural network method, with a good fit to the actual monitoring data and a small relative error. Only one subject out of the 8 spent more time on Chapter B than on Chapter A. Chapter B was more time-consuming than Chapter A. The time spent on Chapter A was more than the time spent on Chapter A. Chapter B was more time-consuming than Chapter A. Chapter B was more time-consuming than Chapter A. One subject spent equal time on both articles. The remaining 6 subjects did not spend as much time on chapter B as they did on chapter A.

Collaborative Optimization Decision Support System for Biomass and Smart Energy Based on Virtual Power Plant

Abstract Energy is an important basic resource for the development of human society. This paper develops a decision support system for collaborative optimization of biomass energy and intelligence using biomass energy and smart energy management technology. Explain the various methods by which biomass can be transformed into usable energy for power plants. A smart energy management model is constructed using Internet of Things technology, and its performance prediction model is predicted using the gray prediction model. To study the optimal allocation of energy in the system constructed in this paper, the objective function and constraints are built using carbon capture technology, and the optimal allocation conditions of energy are calculated. After experimental analysis, it was found that in 2020, biomass energy power generation reached 29 million kilowatts, which was 020.1 million kilowatts higher than in 2015. The co-optimization strategy of biomass energy and smart energy constructed in this paper supports the energy utilization efficiency of the system to reach more than 60%. Compared to the traditional energy division system, the constructed system saves an annual cost of 52.71%. The payback period was shortened to 3.31 years, resulting in a net present value increase of 2.8991 million yuan. The energy synergy system’s power supply produces the least amount of carbon dioxide from an environmental perspective and has been stabilized at 50 kg.

An extended conformable fractional order grey prediction model of per capita electricity consumption for daily life in China

Conformable fractional-order grey prediction models have attracted considerable attention due to their versatile modeling techniques. However, existing models often suffer from limitations in adaptability. To address this, this study proposes a new extended conformable fractional-order grey prediction model, namely the ECFGM(1,1) model. By integrating an adaptive weighting coefficient into the conformable fractional-order accumulation process, the model can effectively prioritize new information, thereby enhancing its rationality and adaptability. Moreover, the adjusted process can be tailored to either emphasize new information or adhere to traditional accumulation methods, which improves its adaptability. To verify the effectiveness of the ECFGM(1,1) model, ECFGM(1,1) is applied to two examples from the literature. The model evaluation results show that the ECFGM(1,1) model has higher fitting accuracy and predictive accuracy than the GM(1,1), CFGM(1,1), and NIPGM(1,1) models. Using the constructed ECFGM(1,1) for predictive analysis of the per capita electricity consumption for daily life in China, the results show that this model can capture the laws of its changes over time. Finally, per capita electricity consumption for daily life in China from 2022 to 2026 is predicted. The results show that by 2026, such consumption is estimated to reach 1165.35 KW.h.

Exploring the machine learning approach to the cultural and ecological sustainable development of Fuzhou tea picking opera

Abstract The article introduces machine learning based on a support vector machine to illustrate the scenario of Fuzhou Tea Casting Opera, a folkloric non-heritage research and ecologically sustainable development, from the perspective of economic return prediction. Fine-grained information recognition and extraction of massive, unstructured UGC data are used for data collection preprocessing in this paper. Mostly, the relationship is established by analyzing the training samples to establish the connection between input and output data. The limited sample information is used to find the best compromise between the model’s complexity and learning ability. Then, search for the parameters that make the geometric interval the largest so as to construct the function and obtain the optimal hyperplane. In this paper, the kernel function is introduced using a ɛ -insensitive loss function. To obtain the decision function in different cases, two cases are modeled using support vector machine regression. Finally, the support vector machine model is used to predict the economic benefits of the Fuzhou tea-picking operation. The SVM prediction model’s prediction error of the economic benefits of tea-picking opera for the nine years from 2010 to 2018 is always maintained at a safe value below 0.035. The accuracy and stability of the prediction effect of the decision tree model and the gray prediction model are not as good as this paper’s model. It shows that the model presented in this paper is more suitable for predicting the benefits of the cultural and ecological sustainable development of Fuzhou tea-picking opera.

Analysis of International Political Economy and Regional Synergy in the Age of Network Informatization

Abstract The world has entered the network information age driven by the information technology revolution, and the international political and economic order is facing turbulent changes. As an important issue in international political economy, this paper builds a model to measure the level of regional synergy development. In the prediction model, the GM(1,1) gray prediction model is chosen to establish the first-order differential equation to predict the trend of synergy development. In the measurement model, the synergistic development index system is built, the entropy weight method is used to determine the weight of the indexes, and the coefficient of variation method is applied to measure the development gap of different cities and territories in the region, and the comprehensive development level of the region is measured. The analysis of regional synergistic development is carried out in the Beijing-Tianjin-Hebei region of China, and the static measure of the overall synergistic degree of the Beijing-Tianjin-Hebei region reaches 0.073 in 2023 and the dynamic measure of the region in 2015-2023 is 0.0911. Beijing is ahead of Hebei and Tianjin in the five regional synergistic dimensions, which are economic, social, ecological, governmental governance, and coordination.