A novel adaptive discrete grey prediction model for forecasting development in energy consumption structure—from the perspective of compositional data

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wuyong Qian ◽  
Hao Zhang ◽  
Aodi Sui ◽  
Yuhong Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Compositional Data ◽  
Development Trend ◽  
Grey Prediction ◽  
Content Type ◽  
Consumption Structure ◽  
Grey Prediction Model ◽  
Proposed Model

PurposeThe purpose of this study is to make a prediction of China's energy consumption structure from the perspective of compositional data and construct a novel grey model for forecasting compositional data.Design/methodology/approachDue to the existing grey prediction model based on compositional data cannot effectively excavate the evolution law of correlation dimension sequence of compositional data. Thus, the adaptive discrete grey prediction model with innovation term based on compositional data is proposed to forecast the integral structure of China's energy consumption. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of China's energy consumption structure is conducted into a future horizon from 2021 to 2035 by using the model.FindingsChina's energy structure will change significantly in the medium and long term and China's energy consumption structure can reach the long-term goal. Besides, the proposed model can better mine and predict the development trend of single time series after the transformation of compositional data.Originality/valueThe paper considers the dynamic change of grey action quantity, the characteristics of compositional data and the impact of new information about the system itself on the current system development trend and proposes a novel adaptive discrete grey prediction model with innovation term based on compositional data, which fills the gap in previous studies.

Application of a novel grey forecasting model with time power term to predict China's GDP

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chong Liu ◽  
Wanli Xie ◽  
Tongfei Lao ◽  
Yu-ting Yao ◽  
Jun Zhang
Keyword(s):  
Economic Development ◽  
Prediction Model ◽  
Future Development ◽  
Development Trend ◽  
Forecasting Model ◽  
Grey Prediction ◽  
Content Type ◽  
Grey Prediction Model ◽  
Grey Forecasting ◽  
Grey Forecasting Model

PurposeGross domestic product (GDP) is an important indicator to measure a country's economic development. If the future development trend of a country's GDP can be accurately predicted, it will have a positive effect on the formulation and implementation of the country's future economic development policies. In order to explore the future development trend of China's GDP, the purpose of this paper is to establish a new grey forecasting model with time power term to forecast GDP.Design/methodology/approachFirstly, the shortcomings of the traditional grey prediction model with time power term are found out through analysis, and then the generalized grey prediction model with time power term is established (abbreviated as PTGM (1,1, α) model). Secondly, the PTGM (1,1, α) model is improved by linear interpolation method, and the optimized PTGM (1,1, α) model is established (abbreviated as OPTGM (1,1, α) model), and the parameters of the OPTGM (1,1, α) model are solved by the quantum genetic algorithm. Thirdly, the advantage of the OPTGM (1,1, α) model over the traditional grey models is illustrated by two real cases. Finally the OPTGM (1,1, α) model is used to predict China's GDP from 2020 to 2029.FindingsThe OPTGM (1,1, α) model is more suitable for predicting China's GDP than other grey prediction models.Originality/valueA new grey prediction model with time power term is proposed.

scholarly journals A Hybrid Grey Prediction Model for Small Oscillation Sequence Based on Information Decomposition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Meng Zhou ◽  
Bo Zeng ◽  
Wenhao Zhou
Keyword(s):  
Prediction Model ◽  
Small Oscillation ◽  
New Method ◽  
Superior Performance ◽  
Small Data ◽  
Grey Prediction ◽  
New Model ◽  
Grey Prediction Model ◽  
Proposed Model ◽  
Oscillation Characteristic

Grey prediction model has good performance in solving small data problem, and has been widely used in various research fields. However, when the data show oscillation characteristic, the effect of grey prediction model performs poor. To this end, a new method was proposed to solve the problem of modelling small data oscillation sequence with grey prediction model. Based on the idea of information decomposition, the new method employed grey prediction model to capture the trend characteristic of complex system, and ARMA model was applied to describe the random oscillation characteristic of the system. Crops disaster area in China was selected as a case study and the relevant historical eight-year data published by government department were substituted to the proposed model. The modelling results of the new model were compared with those of other traditional mainstream prediction models. The results showed that the new model had evidently superior performance. It indicated that the proposed model will contribute to solve small oscillation problems and have positive significance for improving the applicability of grey prediction model.

Analysing the high-tech industry with a multivariable grey forecasting model based on fractional order accumulation

Kybernetes ◽  
2019 ◽  
Vol 48 (6) ◽  
pp. 1158-1174 ◽  
Author(s):  
Liang Zeng
Keyword(s):  
Prediction Model ◽  
Fractional Order ◽  
Guangdong Province ◽  
High Tech ◽  
Grey Prediction ◽  
Content Type ◽  
Grey Prediction Model ◽  
Background Value ◽  
Grey Forecasting ◽  
Optimized Model

PurposeHigh-tech industries play an important role in promoting economic and social development. The purpose of this paper is to accurately predict and analyze the output value of high-tech products in Guangdong Province, China, by using a multivariable grey model.Design/methodology/approachBased on the principle of fractional order accumulation, this study proposes a multivariable grey prediction model. To further enhance the prediction ability and accuracy of the model, an optimized model is established by reconstructing the background value. The optimal parameters are solved by minimizing the average relative error of the system characteristic sequence with the constraint of parameter relationships.FindingsThe results from the study show that the two proposed models exhibit better simulation and prediction performance than the traditional models, while the optimized model can significantly improve the modelling precision. In addition, it is predicted that the output value of high-tech products is 12,269.443bn yuan in 2021, which will approximately double from 2016 to 2021.Research limitations/implicationsThe two proposed models can be used to forecast the trend of the system and are grown as an effective extension and supplement of the traditional multivariable grey forecasting models.Practical implicationsThe forecast and analysis of the development prospects of high-tech industries would be useful for the government departments of Guangdong Province and professional forecasters to grasp the future of high-tech industries and formulate decision planning.Originality/valueA new multivariable grey prediction model based on fractional order accumulation and its optimized model obtained by reconstructing the background value, which can improve the modelling accuracy of the traditional model, is proposed in this paper.

scholarly journals A Multivariate Grey Prediction Model Using Neural Networks with Application to Carbon Dioxide Emissions Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yu-Jing Chiu ◽  
Yi-Chung Hu ◽  
Peng Jiang ◽  
Jingci Xie ◽  
Yen-Wei Ken
Keyword(s):  
Carbon Dioxide ◽  
Prediction Model ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Proposed Model ◽  
Predicted Values ◽  
Carbon Dioxide Co2 ◽  
Grey Relational

The forecast of carbon dioxide (CO2) emissions has played a significant role in drawing up energy development policies for individual countries. Since data about CO2 emissions are often limited and do not conform to the usual statistical assumptions, this study attempts to develop a novel multivariate grey prediction model (MGPM) for CO2 emissions. Compared with other MGPMs, the proposed model has several distinctive features. First, both feature selection and residual modification are considered to improve prediction accuracy. For the former, grey relational analysis is used to filter out the irrelevant features that have weaker relevance with CO2 emissions. For the latter, predicted values obtained from the proposed MGPM are further adjusted by establishing a neural-network-based residual model. Prediction accuracies of the proposed MGPM were verified using real CO2 emission cases. Experimental results demonstrated that the proposed MGPM performed well compared with other MGPMs considered.

The application of trigonometric grey prediction model to average per capita natural gas consumption of households in China

2019 ◽  
Vol 9 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Qiuping Wang ◽  
Subing Liu ◽  
Haixia Yan
Keyword(s):  
Natural Gas ◽  
Prediction Model ◽  
Forecasting Model ◽  
Grey Prediction ◽  
Content Type ◽  
Grey Prediction Model ◽  
Grey Forecasting ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Per Capita

Purpose Due to high efficiency and low carbon of natural gas, the consumption of natural gas is increasing rapidly, and the prediction of natural gas consumption has become the focus. The purpose of this paper is to employ a prediction technique by combining grey prediction model and trigonometric residual modification for predicting average per capita natural gas consumption of households in China. Design/methodology/approach The GM(1,1) model is utilised to obtain the tendency term, then the generalised trigonometric model is used to catch the periodic phenomenon from the residual data of GM(1,1) model for improving predicting accuracy. Findings The case verified the view of Xie and Liu: “When the value of a is less, DGM model and GM(1,1) model can substitute each other.” The combination of the GM(1,1) and the trigonometric residual modification technique can observably improve the predicting accuracy of average per capita natural gas consumption of households in China. The mean absolute percentage errors of GM(1,1) model, DGM(1,1), unbiased grey forecasting model, and TGM model in ex post testing stage (from 2013 to 2015) are 32.5510, 33.5985, 36.9980, and 5.2996 per cent, respectively. The TGM model is suitable for the prediction of average per capita natural gas consumption of households in China. Practical implications According to the historical data of average per capita natural gas consumption of households in China, the authors construct GM(1,1) model, DGM(1,1) model, unbiased grey forecasting model, and GM(1,1) model with trigonometric residual modification. The accuracy of TGM is the best. TGM helps to improve the accuracy of GM(1,1). Originality/value This paper gives a successful practical application of grey model GM(1,1) with the trigonometric residual modification, where the cyclic variations exist in the residual series. The case demonstrates the effectiveness of trigonometric grey prediction model, which is helpful to understand the modeling mechanism of trigonometric grey prediction model.

Geodetic deformation forecasting based on multi-variable grey prediction model and regression model

2019 ◽  
Vol 9 (4) ◽  
pp. 464-471 ◽  
Author(s):  
Erkan Kose ◽  
Levent Tasci
Keyword(s):  
Regression Analysis ◽  
Prediction Model ◽  
Water Level ◽  
Influential Factor ◽  
Grey Prediction ◽  
Estimation Model ◽  
Content Type ◽  
Grey Prediction Model ◽  
Great Consistency ◽  
Deformation Level

Purpose The purpose of this paper is to examine the effectiveness of the multivariable grey prediction model in deformation forecasting. Design/methodology/approach Deformation in a dam can be seen because of many factors but without any doubt, the most influential factor is the water level. In this study, the deformation level of a point in the Keban Dam crest has been tried to be forecasted depending on the water level by the multivariable grey model GM(1,N). Regression analysis was used to test the accuracy of the prediction results obtained using the grey prediction model. Findings The results show that there is a great consistency between the grey prediction values and the actual values, and that the GM(1,N) produces more reliable results than the regression analysis. Based on the results, it can be concluded that the GM(1,N) is a very reliable estimation model for limited data conditions. Originality/value Different from the other studies in the literature, this study investigates deformation in a dam subject to the water level in the dam reservoir. The main contribution of the study to the literature is to suggest a relatively new procedure for estimating the deformation in the dams based on the water level.

Study and Prediction on the Development Trend of Chinese Sports Venues on the Basis of Grey Prediction Model

2014 ◽  
Vol 687-691 ◽  
pp. 1588-1591
Author(s):  
Yu Ming Li
Keyword(s):  
Prediction Model ◽  
Large Scale ◽  
Development Trend ◽  
Survey Method ◽  
Grey Theory ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Sports Events ◽  
Grey Number ◽  
Sports Venues

The grey forecasting method in most of the existing prediction, there are little research on the problem of interval prediction. This paper presents the concept of synthesis of grey number and grey theory, describes the properties of the synthetic ash gray because the grey prediction model is proposed the importance to build the stadium for the first time. On this basis, the thesis analyze the research background and significance, then describes some scene at home and abroad, and then analyzes the important characteristics of large-scale sports events and sports stadium construction, at last, the paper does some prediction about the construction of the stadium. This paper mainly uses the literature investigation method combined with a survey method.

Weapon equipment management cost prediction based on forgetting factor recursive GM (1,1) model

2019 ◽  
Vol 10 (1) ◽  
pp. 38-45
Author(s):  
Subing Liu ◽  
Yin Chunwu ◽  
Cao Dazhi
Keyword(s):  
Prediction Model ◽  
Recursive Estimation ◽  
Model Parameters ◽  
Grey Prediction ◽  
Forgetting Factor ◽  
Content Type ◽  
Grey Prediction Model ◽  
Model Based ◽  
New Information ◽  
Equipment System

Purpose The purpose of this paper is to provide a new recursive GM (1,1) model based on forgetting factor and apply it to the modern weapon and equipment system. Design/methodology/approach In order to distinguish the contribution of new and old data to the grey prediction model with new information, the authors add forgetting factor to the objective function. The purpose of the above is to realize the dynamic weighting of new and old modeling data, and to gradually forget the old information. Second, the recursive estimation algorithm of grey prediction model parameters is given, and the new information is added in real time to improve the prediction accuracy of the model. Findings It is shown that the recursive GM (1,1) model based on forgetting factor can achieve both high effectiveness and high efficiency. Originality/value The paper succeeds in proposing a recursive GM (1,1) model based on forgetting factor, which has high accuracy. The model is applied to the field of modern weapon and equipment system and the result the model is better than the GM(1,1) model. The experimental results show the effectiveness and the efficiency of the prosed method.

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