scholarly journals An Improved Nonequidistant Grey Model Based on Simpson Formula and Its Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiming Hu ◽  
Chong Liu
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Prediction Performance ◽  
Grey System Theory ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey System ◽  
Proposed Model ◽  
Grey Models ◽  
High Prediction

Grey prediction models have been widely used in various fields of society due to their high prediction accuracy; accordingly, there exists a vast majority of grey models for equidistant sequences; however, limited research is focusing on nonequidistant sequence. The development of nonequidistant grey prediction models is very slow due to their complex modeling mechanism. In order to further expand the grey system theory, a new nonequidistant grey prediction model is established in this paper. To further improve the prediction accuracy of the NEGM (1, 1, t2) model, the background values of the improved nonequidistant grey model are optimized based on Simpson formula, which is abbreviated as INEGM (1, 1, t2). Meanwhile, to verify the validity of the proposed model, this model is applied in two real-world cases in comparison with three other benchmark models, and the modeling results are evaluated through several commonly used indicators. The results of two cases show that the INEGM (1, 1, t2) model has the best prediction performance among these competitive models.

scholarly journals A Novel Discrete Grey Model and Its Application

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Wen-Ze Wu ◽  
Jianming Jiang ◽  
Qi Li
Keyword(s):  
Tensile Strength ◽  
Arbitrary Number ◽  
Prediction Accuracy ◽  
Historical Data ◽  
Prediction Performance ◽  
Grey Model ◽  
Original Series ◽  
Proposed Model ◽  
Grey Models ◽  
Future Data

This paper aims to further increase the prediction accuracy of the grey model based on the existing discrete grey model, DGM(1,1). Herein, we begin by studying the connection between forecasts and the first entry of the original series. The results comprehensively show that the forecasts are independent of the first entry in the original series. On this basis, an effective method of inserting an arbitrary number in front of the first item of the original series to extract messages is applied to produce a novel grey model, which is abbreviated as FDGM(1,1) for simplicity. Incidentally, the proposed model can even forecast future data using only three historical data. To demonstrate the effectiveness of the proposed model, two classical examples of the tensile strength and life of the product are employed in this paper. The numerical results indicate that FDGM(1,1) has a better prediction performance than most commonly used grey models.

scholarly journals Forecasting Natural Gas Consumption of China Using a Novel Grey Model

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Chengli Zheng ◽  
Wen-Ze Wu ◽  
Jianming Jiang ◽  
Qi Li
Keyword(s):  
Natural Gas ◽  
Prediction Accuracy ◽  
Prediction Performance ◽  
Grey Model ◽  
Energy Market ◽  
Original Series ◽  
Proposed Model ◽  
Grey Models ◽  
Gas Consumption ◽  
Natural Gas Consumption

As is known, natural gas consumption has been acted as an extremely important role in energy market of China, and this paper is to present a novel grey model which is based on the optimized nonhomogeneous grey model (ONGM (1,1)) in order to accurately predict natural gas consumption. This study begins with proving that prediction results are independent of the first entry of original series using the product theory of determinant; on this basis, it is a reliable approach by inserting an arbitrary number in front of the first entry of original series to extract messages, which has been proved that it is an appreciable approach to increase prediction accuracy of the traditional grey model in the earlier literature. An empirical example often appeared in testing for prediction accuracy of the grey model is utilized to demonstrate the effectiveness of the proposed model; the numerical results indicate that the proposed model has a better prediction performance than other commonly used grey models. Finally, the proposed model is applied to predict China’s natural gas consumption from 2019 to 2023 in order to provide some valuable information for energy sectors and related enterprises.

scholarly journals Forecasting Oil Consumption with Novel Fractional Grey Prediction Model Based on Simpson Formula

2019 ◽  
pp. 1-27
Author(s):  
Xiwang Xiang ◽  
Peng Zhang ◽  
Lang Yu
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Model Error ◽  
Grey System Theory ◽  
Human Society ◽  
Grey Prediction ◽  
Oil Consumption ◽  
Grey System ◽  
Grey Prediction Model ◽  
Grey Models

With the development of human society, the evolving transition of energy will become a common challenge that mankind has to face together. In this context, it is crucial to make scientific and reasonable predictions about energy consumption. This paper presents a novel fractional grey prediction model FGM(1,1,k2) based on the classical fractional grey system theory. In order to improve the prediction accuracy of the FGM(1,1,k2) model, we further analyze the model error and propose improved grey model called as SFGM with optimization of background value. The numerical cases point out that SFGM(1,1,k2) significantly outperforms other existing fractional grey models. Finally, the proposed SFGM(1,1,k2) is applied to the forecasting of oil consumption, the predicted results would provide a reference for making energy policy in new situations.

scholarly journals Forecasting China’s per Capita Living Energy Consumption by Employing a Novel DGM (1, 1, tα) Model with Fractional Order Accumulation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yumu Lu ◽  
Chong Liu ◽  
Haodan Pang ◽  
Ting Feng ◽  
Zijie Dong
Keyword(s):  
Energy Consumption ◽  
Fractional Order ◽  
Prediction Models ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey System ◽  
Annual Increment ◽  
Model Based ◽  
Proposed Model ◽  
Per Capita

The living energy consumption of residents has become an important technical index to promote the economic and social development strategy. The country’s medium- and short-term living energy consumption is featured with both a certainty of annual increment and an uncertainty of random variation. Thus, it can be seen as a typical grey system and shall be suitable for the grey prediction model. In order to explore the future development trend of China’s per capita living energy consumption, this paper establishes a novel grey model based on the discrete grey model with time power term and the fractional accumulation (FDGM (1, 1, tα) for short) for forecasting China’s per capita living energy consumption, which makes the existing model to adapt to different time series by adjusting fractional order accumulation parameter and power term. In order to verify the feasibility and effectiveness of the novel model, the proposed and eight other existing grey prediction models are applied to the case of China’s per capita living energy consumption. The results show that the proposed model is more suitable for predicting China’s per capita energy consumption than the other eight grey prediction models. Finally, the proposed model based on metabolism mechanism is used to predict China’s per capita living energy consumption from 2018 to 2029, which can provide a reference for energy companies or government decision makers.

scholarly journals A Novel Grey Power-markov Model for the Prediction of China's Electricity Consumption

2021 ◽  
Author(s):  
Liqin Sun ◽  
Youlong Yang ◽  
Tong Ning ◽  
Jiadi Zhu
Keyword(s):  
Prediction Models ◽  
Demand Forecasting ◽  
Electricity Consumption ◽  
Original Data ◽  
Development Trend ◽  
Statistical Hypothesis ◽  
Grey Prediction ◽  
New Information ◽  
Proposed Model ◽  
Grey Models

Abstract The grey prediction models of time series are widely used in demand forecasting because only limited data can be used to build the models and no statistical hypothesis is needed. In this paper, a grey power Markov prediction model (RGPMM(λ,1,1)) with time-varying parameters is proposed. This model is based on the principle of “new information priority”, combined with rolling mechanism and Markov theory, and the prediction residual error is modified to further improve the prediction accuracy. Compared with the classic grey models, the new model not only overcomes the inherent defect of poor adaptability to the original data, but also uses real-time information to better reflect the nonlinear characteristics of the original data, so it can be used to describe and predict the nonlinear development trend of things. In order to verify the validity and applicability of the model, the proposed model is used to forecast the total electric consumption in China. The experimental results show that the proposed model has a better prediction effect than other grey models. The proposed model is used to forecast China’s total electricity consumption in the next six years from 2018 to 2023.

Non-equidistant non-homogenous grey prediction model with fractional accumulation and its application

2021 ◽  
pp. 1-14
Author(s):  
Jia-Nian Zhu ◽  
Xu-Chong Liu ◽  
Chong Liu
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Model Based ◽  
Whale Optimization ◽  
High Prediction ◽  
Best Parameters ◽  
Better Than

Non-equidistant non-homogenous grey model (abbreviated as NENGM (1,1, k) model) is a grey prediction model suitable for predicting time series with non-equal intervals. It is widely used in various fields of society due to its high prediction accuracy and strong adaptability. In order to further improve the prediction accuracy of the NENGM (1,1, k) model, the NENGM (1,1, k) model is optimized in terms of the cumulative order and background value of the NENGM (1,1, k) model, and a NENGM (1,1, k) model based on double optimization is established (abbreviated as FBNENGM (1,1, k) model), and the whale optimization algorithm is used to solve the best parameters of the model. In order to verify the feasibility and validity of the FBNENGM (1,1, k) model, the FBNENGM (1,1, k) model and other four prediction models are applied to three cases respectively, and three indexes commonly used to evaluate the performance of prediction models are used to distinguish. The results show that the prediction accuracy of the FBNENGM (1,1, k) model based on double optimization is better than other prediction models.

Research and application of novel Euler polynomial-driven grey model for short-term PM10 forecasting

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiwang Xiang ◽  
Xin Ma ◽  
Minda Ma ◽  
Wenqing Wu ◽  
Lang Yu
Keyword(s):  
Real World ◽  
Pm10 Concentration ◽  
Euler Polynomial ◽  
Grey Model ◽  
Grey System ◽  
Short Term ◽  
Content Type ◽  
New Model ◽  
Proposed Model ◽  
Grey Models

PurposePM10 is one of the most dangerous air pollutants which is harmful to the ecological system and human health. Accurate forecasting of PM10 concentration makes it easier for the government to make efficient decisions and policies. However, the PM10 concentration, particularly, the emerging short-term concentration has high uncertainties as it is often impacted by many factors and also time varying. Above all, a new methodology which can overcome such difficulties is needed.Design/methodology/approachThe grey system theory is used to build the short-term PM10 forecasting model. The Euler polynomial is used as a driving term of the proposed grey model, and then the convolutional solution is applied to make the new model computationally feasible. The grey wolf optimizer is used to select the optimal nonlinear parameters of the proposed model.FindingsThe introduction of the Euler polynomial makes the new model more flexible and more general as it can yield several other conventional grey models under certain conditions. The new model presents significantly higher performance, is more accurate and also more stable, than the six existing grey models in three real-world cases and the case of short-term PM10 forecasting in Tianjin China.Practical implicationsWith high performance in the real-world case in Tianjin China, the proposed model appears to have high potential to accurately forecast the PM10 concentration in big cities of China. Therefore, it can be considered as a decision-making support tool in the near future.Originality/valueThis is the first work introducing the Euler polynomial to the grey system models, and a more general formulation of existing grey models is also obtained. The modelling pattern used in this paper can be used as an example for building other similar nonlinear grey models. The practical example of short-term PM10 forecasting in Tianjin China is also presented for the first time.

scholarly journals Prediction and Optimization of Electrospun Polyacrylonitrile Fiber Diameter Based on Grey System Theory

Materials ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2237 ◽  
Author(s):  
Qihong Zhou ◽  
Liqun Lin ◽  
Ge Chen ◽  
Zhaoqun Du
Keyword(s):  
System Theory ◽  
Prediction Error ◽  
Fiber Diameter ◽  
Grey System Theory ◽  
Grey Prediction ◽  
Grey System ◽  
Average Prediction Error ◽  
Background Value ◽  
High Prediction ◽  
Fitting Error

This paper provides a new method for predicting the diameter of electrospun nanofibers. Based on the grey system theory, the effects of polyacrylonitrile mass fraction, voltage, flow rate, and receiving distance on fiber diameter were studied. The GM(1,1) (grey model) model and DNGM(1,1) (The DNGM (1,1) model is based on the whitening differential equation using parameters to Directly estimate the approximate Non-homogeneous sequence Grey prediction Model) model were established to predict fiber diameter by a single-factor change, and the results showed high prediction accuracy. The multi-variable grey model MGM(1,n) (MGM(1,n) is a Multivariate Grey prediction Model) was used for prediction of fiber diameter when multiple factors change simultaneously. The results showed that the average modeling fitting error is 8.62%. The background value coefficients of the MGM(1,n) model were optimized, the average modeling fitting error was reduced to 1.01%, and the average prediction error was reduced to 1.33%. Combining the fractional optimization with the background-value coefficient optimization, the optimal background-value coefficient α and the order r were selected. The results showed that the average modeling fitting error is 0.85%, and the average prediction error is 0.38%. The results demonstrate that the grey system theory can effectively predict the diameter of polyacrylonitrile electrospinning fibers with high prediction accuracy. This theory can increase the control of nanofiber diameters in production.

Comparison of Two Grey Models Applied in Predicting Numbers of Candidates for College Entrance Examination

2011 ◽  
Vol 105-107 ◽  
pp. 2225-2228
Author(s):  
Gang Li ◽  
Ying Fang ◽  
Ya La Tong
Keyword(s):  
Decision Making ◽  
Prediction Models ◽  
Technical Information ◽  
Grey Model ◽  
Entrance Examination ◽  
Grey Prediction ◽  
College Entrance Examination ◽  
College Entrance ◽  
Grey Models ◽  
The Difference

This paper mainly used two different grey models to predict the numbers of candidates of applying for the college entrance examination. We firstly introduced the conception of GM (1,1,D) and GM (1,1,C), briefly explained the difference between them from the aspect of theory, and then put them into the application of predicting the situation of variance of candidates for college entrance examination. Finally, we analyzed the reasons for this change and countermeasures. Compared to the application of numbers of candidates for college entrance examination by using two grey prediction models, this paper gives an effective method of data analysis, and provides technical information for the relevant decision-making departments. This paper set a good example for the application of selecting the correct grey model to address specific problems.朗读 显示对应的拉丁字符的拼音 字典

Application of Grey Model Theory to Forecast Flight Training Time

2014 ◽  
Vol 635-637 ◽  
pp. 1696-1699
Author(s):  
Jun Wen ◽  
Hai Feng Duan ◽  
Shu Xia Sun ◽  
Ming Xing Li ◽  
Jian Fei Lv
Keyword(s):  
System Theory ◽  
Grey System Theory ◽  
Grey Model ◽  
Flight Training ◽  
Civil Aviation ◽  
Grey System ◽  
Short Term ◽  
Training Time ◽  
Forecast Result ◽  
High Prediction

Grey model GM(1,1) is applied to forecast flight training time. The discreteness of originality data is overcome and the high-precise predicted result is received under the condition of a small amount of data. This paper takes a short-term forecast flight training time of Civil Aviation Flight University of China (CAFUC) by using grey system theory. With a comparison of the actual data to the forecast result, it is proved that using grey system theory to forecast the flight training time of Civil Aviation Flight University of China is feasible with relatively high prediction accuracy.

Sign in / Sign up

Export Citation Format

Share Document