Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and China

Liu, Chong; Wu, Wei; Xie, Wanli; Zhang, Jun

doi:10.1016/j.chaos.2020.110429

Cited by 70 publications

(31 citation statements)

References 42 publications

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“…Comparing equation 2with equation (10) shows that the original GM (1, N) model uses z (1) 1 (k) and N i�2 b i x (1) (1) i (t)dt, respectively, which creates errors.…”

Section: E Basic Multivariate Grey Prediction Modelmentioning

confidence: 99%

“…Grey prediction models play an important role in the grey system theory, which was pioneered by Deng [4]. At present, grey prediction models have been widely used in various fields of society due to their high prediction accuracy and the advantages of small sample modeling [5][6][7][8][9][10][11][12]. Depending on the number of variables required for modeling, grey prediction models can be divided into univariate grey prediction models and multivariable grey prediction models.…”

Section: Introductionmentioning

confidence: 99%

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The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application

2021

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As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM (1, N) model is the most popular and important grey model, in which the first “1” stands for the “first order” and the second “N” represents the “multivariate.” The construction of the background values is not only an important step in grey modeling but also the key factor that affects the prediction accuracy of the grey prediction models. In order to further improve the prediction accuracy of the multivariate grey prediction models, this paper establishes a novel multivariate grey prediction model based on dynamic background values (abbreviated as DBGM (1, N) model) and uses the whale optimization algorithm to solve the optimal parameters of the model. The DBGM (1, N) model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy. It is a grey prediction model with extremely strong adaptability. Finally, four cases are used to verify the feasibility and effectiveness of the model. The results show that the proposed model significantly outperforms the other 2 multivariate grey prediction models.

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“…Comparing equation 2with equation (10) shows that the original GM (1, N) model uses z (1) 1 (k) and N i�2 b i x (1) (1) i (t)dt, respectively, which creates errors.…”

Section: E Basic Multivariate Grey Prediction Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application

2021

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“…On the other hand, some studies used the grey Markov method with rolling mechanism and singular spectrum analysis for energy consumption forecasting like [16] for India. Similarly countrywide studies are [12, for China; [75] for the US; [76] for China and India; [77] for BRICS; [78] for China and US, [79][80] for Brazil, and [81] for Asian countries.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Forecasting Ability for Energy Consumption in BRICS: ARIMA (1,1,1) and FGM (1, 1) Models

Khan¹,

Osińska²

2021

Preprint

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Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to forecast energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the forecasting ability of the FGM(1,1) with traditional ones, like standard GM(1,1) and ARIMA(1,1,1) models. Also, it illustrates the view of BRICS's nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of FGM(1,1) for a specific range of order parameters and the ARIMA(1,1,1) model and the usefulness of both approaches for energy consumption efficient forecasting.

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“…Equation ( 18) is the calculation formula for the degree of relevance. Equation ( 28) is obtained by combining equations (26) and (27). erefore, the G value is uniquely determined, and the R value represents the prediction accuracy; the larger the value, the much higher the precision.…”

Section: Relevance Analysismentioning

confidence: 99%

“…In the combined application of the gray theory and numerical simulation, industry experts have carried out many studies; Pan et al found that the combination of gray theory and numerical simulation can effectively evaluate the deformation of foundation pit excavation and can make real-time prediction [25,26]; Zhang found that the accuracy of numerical simulation was related to the continuity of the gray sequence [27]; Bi et al found that the application of gray prediction and simulation technology could effectively improve model accuracy [28]; Hu et al believed that finite element software analysis of tunnel surrounding rock deformation, combined with gray theory prediction and its mechanical behavior, could effectively change the modeling conditions and accuracy [29,30].…”

Section: Introductionmentioning

confidence: 99%

Research on the Large Deformation Prediction Model and Supporting Measures of Soft Rock Tunnel

Rao

Tao

Xiong

et al. 2020

Advances in Civil Engineering

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The weak surrounding rock has the characteristics of easy softening, poor integrity, low mechanical strength, etc., which makes it easy to induce different degrees of deformation and damage under excavation disturbance and then seriously affects the stability of the tunnel. Carrying out soft rock tunnel deformation prediction research and designing the supporting structure according to the predicted value is of great significance to engineering construction and design. Based on the grey theory, the large deformation of the vault, shoulder, and waist of the soft rock tunnel are predicted, and then the specific bolt support is designed in the maximum predicted value (Smax·R) area. The control effects of different bolts, spacing (d), length (L) on the maximum displacement (Smax·M), and maximum stress (σmax·M) the surrounding rock are analyzed by numerical simulation. Results show that the gray model has high prediction accuracy, the best prediction time is one week, and the maximum error is only 2.99%; with the decrease in d, resin bolt support has a significant supporting effect compared with mortar bolt support, with Smax.M and σmax·M reduced by 64.38% and 10.35%, respectively; as the L of bolt increases, compared with the mortar bolt support, the resin bolt support has a more obvious restraining effect on the surrounding rock deformation, and Smax·M and σmax·M are reduced by 28.20% and 10.00%, respectively; when 4.5 m < L < 6.0 m and 0.6 m < d < 0.7 m, resin bolt support should be adopted; in other ranges, mortar bolt support or resin bolt support has a less significant difference in controlling surrounding rock deformation.

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Application of a novel fractional grey prediction model with time power term to predict the electricity consumption of India and China

Cited by 70 publications

References 42 publications

The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application

The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application

Comparison of Forecasting Ability for Energy Consumption in BRICS: ARIMA (1,1,1) and FGM (1, 1) Models

Research on the Large Deformation Prediction Model and Supporting Measures of Soft Rock Tunnel

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