Application of the novel four-parameter discrete optimized grey model to forecast the wastewater discharged in Chongqing China

PurposeElectricity consumption prediction has been an important topic for its significant impact on electric policies. Due to various uncertain factors, the growth trends of electricity consumption in different cases are variable. However, the traditional grey model is based on a fixed structure which sometimes cannot match the trend of raw data. Consequently, the predictive accuracy is variable as cases change. To improve the model's adaptability and forecasting ability, a novel fractional discrete grey model with variable structure is proposed in this paper.Design/methodology/approachThe novel model can be regarded as a homogenous or non-homogenous exponent predicting model by changing the structure. And it selects the appropriate structure depending on the characteristics of raw data. The introduction of fractional accumulation enhances the predicting ability of the novel model. And the relative fractional order r is calculated by the numerical iterative algorithm which is simple but effective.FindingsTwo cases of power load and electricity consumption in Jiangsu and Fujian are applied to assess the predicting accuracy of the novel grey model. Four widely-used grey models, three classical statistical models and the multi-layer artificial neural network model are taken into comparison. The results demonstrate that the novel grey model performs well in all cases, and is superior to the comparative eight models.Originality/valueA fractional-order discrete grey model with an adaptable structure is proposed to solve the conflict between traditional grey models' fixed structures and variable development trends of raw data. In applications, the novel model has satisfied adaptability and predicting accuracy.

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A Study on the Generalized Approximation Modeling Method Based on Fitting Sensitivity for Prediction of Engine Performance

Discrete Dynamics in Nature and Society ◽

10.1155/2017/5729786 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12

Author(s):

Lin Lin ◽

Fang Wang ◽

Shisheng Zhong

Keyword(s):

Engine Performance ◽

Grey Model ◽

Model Parameters ◽

Bayesian Regularization ◽

Safety Engineering ◽

The Novel ◽

Mean Square ◽

Approximation Model ◽

Mean Square Errors ◽

Fitting Accuracy

Prediction technology for aeroengine performance is significantly important in operational maintenance and safety engineering. In the prediction of engine performance, to address overfitting and underfitting problems with the approximation modeling technique, we derived a generalized approximation model that could be used to adjust fitting precision. Approximation precision was combined with fitting sensitivity to allow the model to obtain excellent fitting accuracy and generalization performance. Taking the Grey model (GM) as an example, we discussed the modeling approach of the novel GM based on fitting sensitivity, analyzed the setting methods and optimization range of model parameters, and solved the model by using a genetic algorithm. By investigating the effect of every model parameter on the prediction precision in experiments, we summarized the change regularities of the root-mean-square errors (RMSEs) varying with the model parameters in novel GM. Also, by analyzing the novel ANN and ANN with Bayesian regularization, it is concluded that the generalized approximation model based on fitting sensitivity can achieve a reasonable fitting degree and generalization ability.

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Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

Journal of Mathematics ◽

10.1155/2021/9200560 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Xiaoshuang Luo ◽

Bo Zeng ◽

Hui Li ◽

Wenhao Zhou

Keyword(s):

Prediction Model ◽

Wind Power ◽

Prediction Models ◽

Grey Model ◽

The Novel ◽

Grey Prediction ◽

Grey Prediction Model ◽

Combination Optimization ◽

Installed Capacity ◽

Novel Model

The intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable solution to meet this challenge. However, the single parameter optimization of the conventional grey model has some limitations in improving its modeling ability. To this end, a novel grey prediction model with parameters combination optimization is proposed in this paper. Firstly, considering the modeling mechanism and process, the order of accumulation generation of the grey prediction model is optimized by Particle Swarm Optimization (PSO) Algorithm. Secondly, as different orders of accumulation generation correspond to different parameter matrixes, the background value coefficient of the grey prediction model is optimized based on the optimal accumulation order. Finally, the novel model of combinational optimization is employed to simulate and forecast Chinese WPIC, and the comprehensive error of the novel model is only 1.34%, which is superior to the other three grey prediction models (2.82%, 1.68%, and 2.60%, respectively). The forecast shows that China’s WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the standpoint of the practitioners and governments.

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Two Novel Grey System Models and Their Applications on Landslide Forecasting

Journal of Control Science and Engineering ◽

10.1155/2016/5727160 ◽

2016 ◽

Vol 2016 ◽

pp. 1-6

Author(s):

De-Yi Ma ◽

Jian-Lin Li

Keyword(s):

Original Data ◽

Small Sample ◽

Grey Model ◽

The Novel ◽

Grey System ◽

Numerical Examples ◽

System Models ◽

Forecasting Models ◽

Landslide Forecasting

For the small sample poor information, grey model is one of the good forecasting models. However, the simulation curve of original data is not consistent with that of the data by translations. In this paper, we present two novel grey system models, that is, generalized grey model and generalized discrete grey model. Compared with grey model, we prove that the simulation curve of original data is consistent with that of the new data by translations for the novel grey model, which was also demonstrated by the results of practical numerical examples.

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An Improved Nonhomogeneous Discrete Grey Model and Its Application

Mathematical Problems in Engineering ◽

10.1155/2020/4638296 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Jianming Jiang ◽

Yu Zhang ◽

Changqing Liu ◽

Wanli Xie

Keyword(s):

Variable Coefficient ◽

Prediction Performance ◽

Grey Model ◽

Small Samples ◽

The Novel ◽

Data Sequence ◽

Grey Models ◽

Modelling Techniques ◽

Natural Gas Demand ◽

Novel Model

In recent years, the nonhomogeneous grey model has received much attention owing to its flexibility and applicability of forecasting small samples. To improve further the prediction accuracy of the nonhomogeneous grey model, this paper is to introduce a new whitening equation with variable coefficient into the original nonhomogeneous grey model, which is abbreviated as ONGM1,1,k,c. First of all, the detailed computational steps of the time response function of the novel model and the restored values of the raw data sequence are deduced through grey modelling techniques. Secondly, two empirical examples from the previous literature are conducted to prove the validity of the novel model. Finally, the novel model is applied to forecast natural gas demand of China, and the results show that the novel model has a better prediction performance compared with other commonly used grey models, including GM1,1, DGM1,1, NGM1,1,k,c, and NGBM1,1.

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Ground Target Tracking Using Aerial Images

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.734-737.2964 ◽

2013 ◽

Vol 734-737 ◽

pp. 2964-2969

Author(s):

Xian Jun Hu ◽

Hang Yu Wang ◽

Zhang Song Shi

Keyword(s):

Differential Equation ◽

Aerial Images ◽

Grey Model ◽

The Novel ◽

Grey Prediction ◽

The Road ◽

Ground Target ◽

Grey Differential Equation ◽

Kalman Prediction ◽

Ground Target Tracking

The tracking of ground targets using aerial images was studied. A improved Kalman filter was derived for the tracking of ground targets. The novel feature of this improved filter were that the grey prediction equations and the road information have been incorporated to improve the accuracy of state estimates.The GM(1,1) (Grey Model) was introduced into Kalman prediction equations.The next value was forecasted by using few forward estimated values with a grey differential equation,which was baesd on the correction of mesurement covariance matrix. The tracking of the targets shows a satisfactory result.

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