Optimal solution for novel grey polynomial prediction model

Wei, Baolei; Xie, Naiming; Hu, Aqin

doi:10.1016/j.apm.2018.06.035

Cited by 98 publications

(49 citation statements)

References 28 publications

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“…The GM(1,1) model is based on the assumption that the original data sequence obeys the exponential distribution [47,61]. However, the practical data sequence often exhibits the characteristic of approximately inhomogeneous exponential growth [48,49].…”

Section: Discussionmentioning

confidence: 99%

Forecasting Electricity Consumption Using an Improved Grey Prediction Model

Zhang

2018

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Prediction of electricity consumption plays critical roles in the economy. Accurate electricity consumption forecasting is essential for policy makers to formulate electricity supply policies. However, limited data and variables generally cannot provide sufficient information to gain satisfactory prediction accuracy. To address this problem, we propose a novel improved grey forecasting model, which combines data transformation for the original data sequence and combination interpolation optimization of the background value of the GM(1,1) model, and is therefore named DCOGM(1,1). To evaluate the simulation and prediction performance of DCOGM(1,1), two case studies are carried out. In addition, the results show that DCOGM(1,1) outperforms most existing improved grey models in terms of forecasting accuracy. Finally, DCOGM(1,1) is employed to predict the total electricity consumption of Shanghai City in China from 2017 to 2021. In addition, the results suggest that DCOGM(1,1) performs well compared with the traditional GM(1,1) model and other grey modification models in this context and Shanghai's electricity consumption will increase stably in the following five years. In summary, DCOGM(1,1) proposed in our study has competent exploration and exploitation ability, and could be utilized as an effective and promising tool for short-term planning for other forecasting issues with limited source data as well.The forecast accuracy of electricity consumption is affected by many factors, such as economic development [8], population [9], power facilities [10] and climate factors [11], and thus the data sequence of electricity consumption often presents to be highly nonlinear, stochastic, time-changeable,

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Section: Discussionmentioning

confidence: 99%

Forecasting Electricity Consumption Using an Improved Grey Prediction Model

Zhang

2018

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“…In this section, four numerical examples, which come from the annual per capita electricity consumption prediction problem [26], the energy consumption prediction problem [18], the natural gas consumption prediction problem [13,33], and the total output value of construction industry prediction problem, respectively, are adopted to show the high precision of the new WFGM(1,1) model. To better show its advantages, numerical results of the WFGM(1,1) model are compared with those of the original GM(1,1) model, the fractional GM(1,1) model (FGM(1,1)) [17], and the new information priority GM(1,1) model (NIPGM(1,1)) [18] studied recently.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…To improve the prediction accuracy of the GM(1,1) model, many researchers have carried out a lot of works from di erent aspects, such as nding new accumulation generating operators [14][15][16][17][18][19], constructing more accurate background value formula [20,21], choosing parameter optimization methods [22], improving initial guess [23], and reducing residuals based on Fourier analysis and Markov chain [9,20]. Recently, some nonhomogeneous, nonlinear, hybrid, and multivariable grey models are proposed, see [6,[24][25][26] for examples. Modeling mechanism analysis can be found in [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

A Novel Weighted Fractional GM(1,1) Model and Its Applications

et al. 2020

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Based on the ideas of the new information priority principle and the fractional-accumulation generating operator, in this paper we propose a novel weighted fractional GM(1,1) (WFGM(1,1)) prediction model. In the new model, the original sequence is first transformed by using the weighted fractional-accumulation generating operator, which involves two parameters. With special choices of these parameters, the proposed WFGM(1,1) model reduces to the classical GM(1,1) model and the fractional GM(1,1) (FGM(1,1)) model, as well as the new information priority GM(1,1) (NIPGM(1,1)) model studied recently. Stability property of the WFGM(1,1) model is studied in detail. In practice, the quantum particle swarm optimization algorithm is adopted to choose the quasi-optimal parameters for the new model so as to get the best fitting accuracy. Finally, four numerical examples from different practical applications are present. Numerical results show that the new proposed prediction model is very efficient and has both the best fitting accuracy and the best prediction accuracy compared with the GM(1,1) and the FGM(1,1) as well as the NIPGM(1,1) prediction models.

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“…In the meanwhile, the linear parameters are also obtained when the optimal value α is substituted into equation (19). is strategy has been utilized to search for the optimum order of the fractional grey prediction model [25,36].…”

Section: Complexitymentioning

confidence: 99%

A Novel Power‐Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China

Zhang

She

2019

Complexity

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Along with the improvement of Chinese people’s living standard, the proportion of residential energy consumption in total energy consumption is rapidly increasing in China year by year. Accurately forecasting the residential energy consumption is conducive to making energy programming and supply plan for the administrative departments or energy companies. By improving the grey action quantity of traditional grey model with an exponential time term, a novel power-driven grey model is proposed to forecast energy consumption as reference data for decision makers. The nonlinear parameter of power-driven grey action quantity is a crucial factor to influence the prediction precision. To promote the prediction accuracy of the power-driven grey model, whale optimization algorithm is adopted to seek for the optimal value of the nonlinear parameter. Two validations on real-world datasets are conducted, and the results indicate that the power-driven grey model has significant advantages on the aspect of prediction performance compared with the other seven classical grey prediction methods. Finally, the power-driven grey model is applied in forecasting the total residential energy and the thermal energy consumption of China.

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Optimal solution for novel grey polynomial prediction model

Cited by 98 publications

References 28 publications

Forecasting Electricity Consumption Using an Improved Grey Prediction Model

Forecasting Electricity Consumption Using an Improved Grey Prediction Model

A Novel Weighted Fractional GM(1,1) Model and Its Applications

A Novel Power‐Driven Grey Model with Whale Optimization Algorithm and Its Application in Forecasting the Residential Energy Consumption in China

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