Metabolic Grey Model Research Articles

An innovative MGM–BPNN–ARIMA model for China’s energy consumption structure forecasting from the perspective of compositional data

Effective forecasting of energy consumption structure is vital for China to reach its “dual carbon” objective. However, little attention has been paid to existing studies on the holistic nature and internal properties of energy consumption structure. Therefore, this paper incorporates the theory of compositional data into the study of energy consumption structure, which not only takes into account the specificity of the internal features of the structure, but also digs deeper into the relative information. Meanwhile, based on the minimization theory of squares of the Aitchison distance in the compositional data, a combined model based on the three single models, namely the metabolism grey model (MGM), back-propagation neural network (BPNN) model, and autoregressive integrated moving average (ARIMA) model, is structured in this paper. The forecast results of the energy consumption structure in 2023–2040 indicate that the future energy consumption structure of China will evolve towards a more diversified pattern, but the proportion of natural gas and non-fossil energy has yet to meet the policy goals set by the government. This paper not only suggests that compositional data from joint prediction models have a high applicability value in the energy sector, but also has some theoretical significance for adapting and improving the energy consumption structure in China.

Prediction of machining errors for free-form surfaces based on an improved grey model (1,N)

An improved grey model (GM) for predicting free-form surface machining errors was established to address the low measuring efficiencies of coordinate measuring machines (CMMs) and the low prediction accuracy of the GM(1,1) model. A number of points on a free-form surface are measured with a CMM, and machining errors are obtained. Ideas from metabolic methods and the GM(1,N) prediction model were combined. To reduce the impact of random fluctuations of the machining errors, a Markov prediction model was then used to correct the fitted results for the residuals and obtain the predicted results of the machining errors for free-form surfaces, thus improving the prediction accuracy of the model. The predicted results of the metabolic GM(1,1) and metabolic GM(1,N) models were then compared. The experimental results showed that the combination of metabolic theory, a grey model, and Markov theory effectively improved the prediction accuracy.

Research on emergency material demand based on urgency and satisfaction under public health emergencies.

In recent years, the frequent occurrence of public health emergencies has had a significant impact on people's life. The study of emergency logistics has also attracted scholars' attention. Therefore, matching emergency materials' supply and demand quickly, which meets urgency and satisfaction, is the purpose of this paper. This paper used the Metabolism Grey Model (1,1) (GM (1,1)) and the material demand prediction model to predict the number of infections and material demand. Besides, we established a bi-objective optimization model by constructing a profit and loss matrix and a comprehensive utility perception matrix. The results show that the method is helpful in matching the optimal supply and demand decision quickly on the basis of meeting urgency and satisfaction. The method is helpful in improving the fairness of emergency material distribution, which could better protect people's livelihoods.

Short-term Forecast of EV Ownership in Shanghai Based on Metabolic GM(1,1)-Markov Model

The reasonable forecast of Electric Vehicle (EV) ownership is conducive to grasping the market development and determining the development goals of EVs. In addition, predicting the scale of EVs is conducive to analyzing the impact of EV charging on power grid load and guiding the construction of EV charging stations and other infrastructure. In view of the low amount of EV ownership data over the years in Shanghai, an improved grey model(GM)-Markov prediction method is proposed to predict the ownership of EVs. Firstly, several sets of data at recent time points are selected and the metabolic GM(1,1) replaces the original GM model for data fitting. After obtaining the fitting results, a group of data with the highest fitting accuracy was selected to establish the Markov correction model. Secondly, the Markov correction model is established for the highest fitting accuracy data. The sample mean-standard deviation method is used to divide the state matrix, and the interval partition criterion is dynamically adjusted to fit the relative error distribution probability, which avoids certain subjectivity. Compared with the original model, the relative error of fitting results decreases from 4.780% to 0.764%, and the prediction accuracy of the modified model is further improved. Finally, the ownership of EVs in Shanghai by the Metabolic GM(1,1) -Markov model in the next three years is forecasted, and the ownership of EVs in Shanghai is expected to exceed 1 million in 2023.

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.

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 Metabolism Grey Prediction Model Based on Big Data and Internet of Things Technology

In view of the uncertainty and diversity of the metabolic grey prediction model in the prediction process, resulting in poor prediction effect, a metabolic grey prediction model based on big data and Internet of things technology is constructed. Establish the aerobic metabolic process of human telecontrol and put forward the detection index of aerobic metabolic cycle function in human telecontrol; on this basis, use the metabolic grey prediction model analysis algorithm to determine the active intrusion intention of complex network, establish the intrusion intention attack behavior set function, establish the internal operation architecture under the technology of big data and Internet of things, and realize the construction of metabolic grey prediction model. The experimental results show that the constructed model can realize data prediction, with high confidence level and good effect.

Prediction of variable amplitude fatigue crack growth life based on modified grey model

Grey model has been several applications on fatigue crack, with prediction on fatigue crack growth life curve often serving as milestones. A typical difficulty in predicting the fatigue crack growth life curve is the retardation of crack growth caused by the overload effect. Overload retardation, resulting in the lack of experiment data points in the retardation interval and the deflection of crack growth curve, is a long-term challenge for predicting fatigue crack growth. We improve the ability of grey model to predict fatigue crack growth life by second order B-spline interpolation, an interpolation for local data points. It combines the trend of local data point to deal with the retardation interval, and interpolates multiple data points in the retardation interval when there is a lack of local data, and uses a metabolic grey model to construct the prediction formula of crack growth. In the study involving variable amplitude load and multiple overload fatigue crack growth experiments, the proposed method is proved to improve, with statistical significance, the predictive ability on the whole range of experiment data. The method is simple and accurate. Consequently, it is conducive to solve the problem of fatigue crack growth life of engineering structure.

Fault Detection and Isolation of the Multi-Sensor Inertial System.

In order to solve the problem that the generalized likelihood test method cannot isolate the single fault of the four-gyro system and the double faults of the six-gyro system, a fault detection and isolation method combining the generalized likelihood test method with the residual error of the metabolism grey model is presented. The problem of isolating the single fault of the four-gyro system and the double faults of the six-gyro system using the generalized likelihood test method is analyzed. The method and process of fault detection and isolation are designed. The validity of the method presented in this paper is verified by simulation tests of the single fault of the four-gyro system and the double faults of the six-gyro system. By comparing the isolation performance with the generalized likelihood test method, it is proved that the isolation performance of the method proposed in this paper is better than that of the generalized likelihood test method. The method mentioned in this paper can effectively realize fault detection and isolation of the multi-gyro system and improve the inertial system’s reliability.

Exploring the industrial solid wastes management system: Empirical analysis of forecasting and safeguard mechanisms

Industrial solid wastes (ISWs) not only destroys the ecological environment, but also seriously affects human health, which is one of the main obstacles to sustainable development. Consequently, Effective management of ISWs is essential to support efforts to achieve cleaner production and ecological upgrading of industrial structure. In this study, metabolic grey model (MGM (1,1)) is adopted to forecasting the ISWs generation and treatment in China. Meanwhile, we develop an ISWs management system involving its safeguard mechanisms. Forecasting results show that China's ISWs generated have been a slowly increasing trend from 2018 to 2025, which will be controlled between 389819 million tons and 488002 million tons, and the utilization, disposal and storage of ISWs have a significant upward trend. However, the ratio of ISWs utilized will eventually remain at around 50% in the future. According to the prediction results, the application of this ISWs management system can increase the efficiency of waste recycling and reuse, and make ISWs become renewable resources. Research results also illustrate that the safeguard mechanisms, including government policy tools, collaborative agents of the industry-university, green technology innovation, and circulation of green products, have ensured a highly efficient recycling and beneficial waste management to create more added values for the ISWs materials.

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

Forecasting energy demand is the basis for sustainable energy development. In recent years, the new discovery of East Africa’s energy has completely reversed the energy shortage, having turned the attention of the world to the East African region. Systematic research on energy forecasting in Africa, particularly in East Africa, is still relatively rare. In view of this, this study uses a variety of methods to comprehensively predict energy consumption in East Africa. Based on the traditional grey model, this study: (1) Integrated the power coefficient and metabolic principles, and then proposed non-linear metabolic grey model (NMGM) forecasting model; (2) Used Auto Regressive Integrated Moving Average Model (ARIMA) for secondary modeling, and then developed a metabolic grey model-Auto Regressive Integrated Moving Average Model (MGM-ARIMA) and non-linear metabolic grey model-Auto Regressive Integrated Moving Average Model (NMGM-ARIMA) combined models. In terms of the prediction interval, the data for 2000–2017 is a fit to the past stage, while the data for 2018–2030 is used for the prediction of the future stage. To measure the effect of the prediction, the study used the average relative error indicator to evaluate the accuracy of different models. The results indicate that: (1) Mean relative errors of NMGM, MGM-ARIMA, and NMGM-ARIMA are 2.9697%, 2.0969%, and 1.4654%, proving that each prediction model is accurate; (2) Compared with the single model, the combined model has higher precision, confirming the superiority and feasibility of model combination. After prediction, the conclusion shows that East Africa’s primary energy consumption will grow by about 4 percent between 2018 and 2030. In addition, the limitation of this study is that only single variable are considered.

Forecasting of Turkey's greenhouse gas emissions using linear and nonlinear rolling metabolic grey model based on optimization

Abstract This study aims to contribute to the development of energy policies for Turkey's greenhouse gas (GHG) emissions in the future. For this purpose, linear and nonlinear metabolic grey models are combined with the optimization technique to obtain more accurate forecasting results. Optimization technique provides estimation of the parameters in metabolic grey model (MGM(1,1)) and nonlinear metabolic grey model (NMGM(1,1)). In this study, MGM(1,1), NMGM(1,1), optimized metabolic grey model OMGM(1,1), and optimized nonlinear metabolic grey model (ONMGM(1,1)) are applied for prediction of Turkey's GHG emissions including the Land Use, Land Use Change and Forestry (LULUCF), excluding LULUCF and from the energy sector. The ONMGM(1,1) gives more accurate results than the others from 1995 to 2016. The MAPE values of the ONMGM(1,1) are 4.80%, 4.14% and 5.19% for Turkey's GHG emissions with LULUCF, without LULUCF and from the energy sector, respectively. On the other hand, results of the ONMGM(1,1) show that the annual growth rates are forecasted as 0.56%, 0.66% and 0.49% for Turkey's GHG emissions with LULUCF, without LULUCF and from the energy sector, respectively, from 2017 to 2025. Furthermore, Turkey's highest GHG emissions with LULUCF, without LULUCF and from the energy sector are estimated by MGM(1,1) as 606.9 Mt of CO2 equivalent, 726.4 Mt of CO2 equivalent and 585.2 Mt of CO2 equivalent in 2025, respectively. According to Turkey's Intended Nationally Determined Contribution (INDC), target of its GHG emissions including LULUCF is estimated as 934 Mt of CO2 equivalent in a Business-As-Usual scenario and 790 Mt of CO2 equivalent in a Mitigation scenario for the year 2025. Therefore, this study presents lower values of Turkey's GHG emissions with LULUCF than the values of Turkey's INDC for the year 2025. Additionally, results of this study present that energy sector has the largest share of Turkey's GHG emissions from 2017 to 2025. Therefore, Turkish Government should develop policies to use energy more efficiently and to increase the capacity of renewable energy sources in electricity generation. Especially, Turkey's ambition to increase the share of renewable energy in total is very assertive. This can be seen from the Turkey's 11th Development Plan. According to the Plan, the share of total renewable energy and natural gas in total electricity generation is going to be increased from 32.5% in 2018 to 38.8% in 2023 and to be decreased from 29.85% in 2018 to 20.7% in 2023, respectively.

Forecasting India’s Electricity Demand Using a Range of Probabilistic Methods

With serious energy poverty, especially concerning power shortages, the economic development of India has been severely restricted. To some extent, power exploitation can effectively alleviate the shortage of energy in India. Thus, it is significant to balance the relationship between power supply and demand, and further stabilize the two in a reasonable scope. To achieve balance, a prediction of electricity generation in India is required. Thus, in this study, five methods, the metabolism grey model, autoregressive integrated moving average, metabolic grey model-auto regressive integrated moving average model, non-linear metabolic grey model and non-linear metabolic grey model-auto regressive integrated moving average model, are applied. We combine the characteristics of linear and nonlinear models, making a prediction and comparison of Indian power generation. In this way, we enrich methods for prediction research on electrical energy, which avoids large errors in trends of electricity generation due to those accidental factors when a single predictive model is used. In terms of prediction outcomes, the average relative errors from five models above are 1.67%, 1.62%, 0.84%, 1.84%, and 1.37%, respectively, which indicates high accuracy and reference value of these methods. In conclusion, India’s power generation will continue to grow with an average annual growth rate of 5.17% in the next five years (2018–2022).

Filtering for Drift Data of a Laser Doppler Velocimeter Based on Metabolic Time-Series–Grey Model

To reduce the random error of the output data of a laser Doppler velocimeter (LDV) and improve its measurement accuracy, a new method to filter the drift data of the LDV is proposed: a metabolic time-series–grey model that combines a metabolic time-series model with a metabolic grey model. The basic principle is first introduced. Then, the metabolic time-series–grey model is applied to filter the drift data of an LDV and compared with the metabolic time-series model and the metabolic grey model. The variance analysis and the Allan variance are used to analyze the drift data before and after being modeled and filtered. The results show that the metabolic time-series–grey model can effectively reduce the random error of the LDV in real time and greatly improve its measurement accuracy, and its filtering effect outperforms that of the metabolic time-series model and the metabolic grey model.

Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM

To more accurate forecast China's oil consumption, the major driving force of global new added oil demand, a new nonlinear-dynamic grey model is developed, namely NMGM (1, 1, α). The proposed NMGM (1, 1, α) upgrades the nonlinear grey model (GM) from stationary to dynamic model through effectively integrating nonlinear forecasting technique and the biological metabolism idea. The proposed NMGM (1, 1, α), and other three existing grey models (linear GM (1, 1), nonlinear GM (1, 1, α), metabolism GM (1, 1)) are run respectively to simulate and forecast Chinese oil consumption from 1990 to 2026. The simulation results show that our proposed NMGM (1, 1, α) are higher accurate than the other three models. In addition to better forecast energy consumption, the proposed NMGM (1, 1, α) also can be used to forecast in other fields. The modeling results based on the NMGM (1, 1, a) show that China oil consumption in the next decade (2017–2026) will be increased by 51%. The better forecasting Chinese oil consumption by using the proposed model can provide useful information for the researchers, policymakers and others stakeholders in Chinese and global oil market.

A novel diagnostic and prognostic framework for incipient fault detection and remaining service life prediction with application to industrial rotating machines

Data-driven machine health monitoring systems (MHMS) have been widely investigated and applied in the field of machine diagnostics and prognostics with the aim of realizing predictive maintenance. It involves using data to identify early warnings that indicate potential system malfunctioning, predict when system failure might occur, and pre-emptively service equipment to avoid unscheduled downtime. One of the most critical aspects of data-driven MHMS is the provision of incipient fault diagnosis and prognosis regarding the system’s future working conditions. In this work, a novel diagnostic and prognostic framework is proposed to detect incipient faults and estimate remaining service life (RSL) of rotating machinery. In the proposed framework, a novel canonical variate analysis (CVA)-based monitoring index, which takes into account the distinctions between past and future canonical variables, is employed for carrying out incipient fault diagnosis. By incorporating the exponentially weighted moving average (EWMA) technique, a novel fault identification approach based on Pearson correlation analysis is presented and utilized to identify the influential variables that are most likely associated with the fault. Moreover, an enhanced metabolism grey forecasting model (MGFM) approach is developed for RSL prediction. Particle filter (PF) is employed to modify the traditional grey forecasting model for improving its prediction performance. The enhanced MGFM approach is designed to address two generic issues namely dealing with scarce data and quantifying the uncertainty of RSL in a probabilistic form, which are often encountered in the prognostics of safety-critical and complex assets. The proposed CVA-based index is validated on slowly evolving faults in a continuous stirred tank reactor (CSTR) system, and the effectiveness of the proposed integrated diagnostic and prognostic method for the monitoring of rotating machinery is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump and one case study of an operational centrifugal compressor.

India's dependence on foreign oil will exceed 90% around 2025 - The forecasting results based on two hybridized NMGM-ARIMA and NMGM-BP models

Few study has been conducted to forecast India's dependence on foreign oil, although India is the world's third-largest oil consumer and exporter, making it a key player in the oil market. This work is aimed to accurately forecast India's dependence on foreign oil. To develop better forecasting techniques, this study used linear Auto Regressive Integrated Moving Average (ARIMA), and nonlinear Back Propagation (BP) to correct nonlinear metabolic grey model (NMGM) forecasting residuals in three steps: (i) integrating the metabolic idea with a nonlinear grey model to develop NMGM, (ii) integrating the proposed NMGM with ARIMA to develop NMGM-ARIMA, and (iii) integrating the proposed NMGM with BP to develop NMGM-BP. To further improve forecasting accuracy, this work forecasted the India's dependence on foreign oil from two perspectives: (i) direct forecasting the change of ratio of imports oil to total oil consumption in India, (ii) indirect forecasting, i.e., the difference between the forecasted oil consumption and production was divided by the forecasted oil production. The proposed NMGM-ARIMA and NMGM-BP were used to fit the India's dependence on foreign oil for 1995–2017 and forecast the data for 2018–2030. The mean relative error of the proposed forecasting models was around 1.5%, which could produce a convincing forecasting results. Our forecasting results show that India's dependence on foreign oil will reach 90% around 2025, which poses serious challenge to Indian oil security and the global oil market.

Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States

Pennsylvania and Texas accounted for about 60% of U.S. total shale gas production. Better forecasting shale gas production in Pennsylvania and Texas can serve us to better predict U.S. shale gas production. In this work, we integrate the linear and nonlinear forecasting techniques in order to use the advantages and avoid the disadvantages of linear and nonlinear forecasting models, so as to improve forecasting accuracy. Specifically, we develop two hybrid forecasting techniques, i.e., nonlinear metabolic grey model–Autoregressive Integrated Moving Average Model (NMGM-ARIMA), and Autoregressive Integrated Moving Average Model - Artificial neural network (ARIMA-ANN). 60 samples (monthly shale gas production in Pennsylvania and Texas) are used to test these two proposed forecasting techniques and these existing single nonlinear (NMGM, and ANN) and linear (ARIMA) forecasting techniques. The results show that for samples from either Pennsylvania or Texas, the mean absolute percent error of NMGM-ARIMA (3.16%, 1.64%) is smaller than that of NMGM (4.31%, 2.98%) and ARIMA (3.53%, 2.03%), and that of ARIMA-ANN (2.06%, 1.38%) is also smaller than ARIMA (3.53%, 2.03%) and ANN (3.09%, 1.71%). The proposed hybrid NMGM-ARIMA and ARIMA-ANN can achieve more accurate forecasting effect than the single theory-based models that made them up, and can be used in forecasting other fuels. The forecasting results show growth rates of shale gas production in Pennsylvania is higher than Texas in 2017 and 2018.

Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models

Africa has abundant energy resources, but African energy research level is relatively low. In response to this gap, this paper takes Middle Africa as an example to systematically predict energy demand to give support. In this paper, we utilize four models, metabolic grey model (MGM), modified exponential curve method (MECM), autoregressive integrated moving average (ARIMA) and BP neural network model (BP), to predict the energy consumption of Middle Africa in the next 14 years. Comparing four completely different types of predictive models can fully depict the characteristics of the predictive data and give an all-round analysis of the predicted results. These proposed models are applied to simulate Middle Africa’s energy consumption between 1994 and 2016 to test their accuracy. Among them, the mean absolute percent error (MAPE) of MGM, MECM, ARIMA and BP are 2.41%, 4.80%, 1.91%, and 0.88%. The results show that MGM, MECM, ARIMA, and BP presented in this paper can produce reliable forecasting results. Therefore, the four models are used to forecast energy demand in the next 14 years (2017–2030). Forecasts show that energy demand of Middle Africa will continue to grow at a rate of about 5.37%.

Forecasting Coal Consumption in India by 2030: Using Linear Modified Linear (MGM-ARIMA) and Linear Modified Nonlinear (BP-ARIMA) Combined Models

India’s coal consumption is closely related to greenhouse gas emissions and the balance of supply and demand in energy trading markets. Most existing research on India focuses on total energy, renewable energy and energy intensity. To fill this gap, this study used two single forecasting models: the metabolic grey model (MGM) and the Back-Pro-Pagation Network (BP) to make predictions. In addition, based on these two single models, this study also developed the ARIMA correction principle and derived two combined models: the metabolic grey model, the Autoregressive Integrated Moving Average model (MGM-ARIMA) and Back-Pro-Pagation Network; and the Autoregressive Integrated Moving Average model (BP-ARIMA). After fitting India’s coal consumption during 1995–2017, the average relative errors of the four models were 2.28%, 1.53%, 1.50% and 1.42% respectively. The forecast results show that coal consumption in India will continue to increase at an average annual rate of 2.5% during the period from 2018–2030.