Forecasting the hydroelectricity consumption of China by using a novel unbiased nonlinear grey Bernoulli model

The average generation of electricity is getting increased day by day due to its increasing demand. So forecasting the future needs of electricity is very essential, especially in India. In this paper, a Grey Model (GM) and a Nonlinear Grey Model (NGM) are introduced with the concept of the Bernoulli Differential Equation (BDE) to obtain higher predictive precision, accuracy rate. To improve the prediction accuracy of GM, the Nonlinear Grey Bernoulli Model (NGBM) is used. The NGBM model is having the capability to produce more reliable outcomes. The NGBM with power r is a nonlinear differential equation. Using power r in NGBM the expected result can be controlled and adjusted to fit the results of 1-AGO historical raw data. NGBM is a recent grey prediction model to easily adjust for the correctness of GM(1, 1) stable with a BDE. The differentiation of desired outcome with the actual GM(1, 1) has been displayed through a feasible forecasting model NGBM(1, 1) by accumulating the decisive variables. This model may help government to extend future planning for generation of electricity.

The grey prediction is a common method in the prediction. Studies show that general grey models have high modeling precision when the time sequence varies slowly, but some grey models show low modeling precision for the high-growth sequence. The paper researches the grey modeling for the high-growth sequence using the extended nonlinear grey Bernoulli model NGBM(1,1,t⌃p,α). To improve the nonlinear grey Bernoulli model NGBM(1,1,t⌃p,α)'s prediction precision and make data have better adaptability to the model, the paper makes improvements in the following three aspects: (1) the paper improves the accumulated generating sequence of original time sequence, i.e. making a new transformation of traditional accumulated generating sequence; (2) the paper improves the model's structure, extends the grey action and builds an extended nonlinear grey Bernoulli model NGBM(1,1,t⌃p,α); (3) the paper improves the model's background value and uses the value of cubic spline function to approximate the background value. Because the parameters of the new accumulated generating sequence transformed, the nonlinear grey Bernoulli model's time response equation and the background value are optimized simultaneously, the prediction precision increases greatly. The paper builds an extended nonlinear grey Bernoulli model NGBM(1,1,t⌃2,α) using the method proposed and seven comparison models for China's express delivery volume per capita. Comparison results show that the extended nonlinear grey Bernoulli model built with the method proposed has high simulation and prediction precision and shows the precision superior to that of seven comparison models.

PurposeThe widespread application of traditional grey model (GM) in different academic fields such as electrical engineering, education, mechanical engineering and agriculture provided the authors with an incentive to conduct the present empirical research in an accounting field, in particular, auditing practice. In this regard, the purpose of this paper is to employ the nonlinear type of the original GM to forecast the drastically changed data on audit reports, primarily due to the fact that the linear nature of GM is unable to forecast nonlinear data precisely. In essence, this paper adds value to the strand of audit report literature by examining the impact of different financial ratios on auditors’ opinion and then forecasting audit reports by employing GMs.Design/methodology/approachThe grey forecasting model is known as a system containing uncertain information presented by grey numbers, equations and matrices. The grey forecasting model is employed by using a differential equation in an uncertain system with limited data set which is suitable for smoothing discrete data. In addition, the analyses are conducted by applying a sample of top 50 listed companies on the Tehran Stock Exchange during 2011-2016.FindingsThe findings suggest that audit reports are most influenced by the current ratio and conversely, least influenced by the ratio of working capital turnover. Moreover, the authors argue that the Nash nonlinear grey Bernoulli model is more precise than the nonlinear grey Bernoulli model and GM in forecasting audit reports.Originality/valueThe current study may give more strength to stakeholders in order to analyse and forecast audit report.

Forecasting of foreign exchange rates of Taiwan’s major trading partners by novel nonlinear Grey Bernoulli model NGBM(1, 1)

Parameter optimization of nonlinear grey Bernoulli model using particle swarm optimization

The nonlinear grey Bernoulli model uses the first-order accumulated generating operation (1-AGO) to accumulate the sequence. However, 1-AGO violates the principle of new information priority, and the prediction accuracy needs to be improved. For this purpose, this paper proposed a conformable fractional non-homogeneous grey Bernoulli model by combing the conformable fractional accumulation operator and the non-homogeneous grey Bernoulli model to forecast biofuels production. The article discussed the properties of the proposed model and proved that the novel model is a more general extension of other grey models. The Salp Swarm Algorithm was used to optimize the nonlinear multi-objective parameters and improve prediction accuracy. Furthermore, three examples were used to verify the proposed model’s performance capability, which verified that the competitiveness of the new model is a better grey model. Then the novel model could be used to predict biofuels production in the US and China. Biofuels production from 2009 to 2016 was used to construct models, and the data from 2017 to 2020 was used to verify the model’s accuracy. The results indicated that the proposed model has higher accuracy in terms of prediction. Finally, the novel model could predict biofuels production of the above countries from 2021 to 2025. The forecast results suggested that biofuels production in the US will change moderately. And in China, biofuels production will show an increasing trend in the next few years.

Forecasting short-term renewable energy consumption of China using a novel fractional nonlinear grey Bernoulli model

Application of a novel nonlinear multivariate grey Bernoulli model to predict the tourist income of China

In order to improve the prediction performance of the existing nonlinear grey Bernoulli model and extend its applicable range, an improved nonlinear grey Bernoulli model is presented by using a grey modeling technique and optimization methods. First, the traditional whitening equation of nonlinear grey Bernoulli model is transformed into its linear formulae. Second, improved structural parameters of the model are proposed to eliminate the inherent error caused by the leap jumping from the differential equation to the difference one. As a result, an improved nonlinear grey Bernoulli model is obtained. Finally, the structural parameters of the model are calculated by the whale optimization algorithm. The numerical results of several examples show that the presented model’s prediction accuracy is higher than that of the existing models, and the proposed model is more suitable for these practical cases.

A new grey prediction model and its application to predicting landslide displacement

One of the most critical solution for tackling the challenges of global warming and climate change is to study and know the accurate prediction of carbon dioxide (CO2) emissions. Thus, aid to develop appropriate strategic plans that will reduce future damages caused by these emissions into the atmosphere. This study utilizes annual time series data on CO2 emissions in Saudi Arabia from 1970 to 2016. The goal of this study is to predict CO2 emissions using the Nonlinear Grey Bernoulli model NGBM (1,1), and compared with the GM (1,1) model based on MAPE metrics to achieve a high-accuracy prediction. The NGBM (1,1) is a newly created grey model with wide ranging applications in diverse fields due to its precision in handling small time-series datasets with nonlinear variations. The NGBM (1,1) with power γ is a nonlinear differential equation that can control the predicted result and adjust the solution to fit the 1-AGO of previous raw data. Thus, the findings show that at sample sizes of N=10 and N=5, the Nonlinear Grey Bernoulli Model (NGBM) is more precise than the Grey Model GM (1, 1). The findings could help the government develop future economic policies.

A novel composite forecasting framework by adaptive data preprocessing and optimized nonlinear grey Bernoulli model for new energy vehicles sales

Forecasting Taiwan’s major stock indices by the Nash nonlinear grey Bernoulli model

Forecasting the cumulative number of confirmed cases of COVID-19 in Italy, UK and USA using fractional nonlinear grey Bernoulli model.

PurposeThe interval number prediction of power generation can provide a reference for the rational planning of the power system. For the nonlinearity, uncertainty and complex trends of power generation in East China, a matrixed nonlinear grey Bernoulli model combined with the weighted conformable fractional accumulation generating operator (MWCFNGBM(1,1,tα)) is proposed.Design/methodology/approachFirst, the original sequence fluctuations are smoothed with the weighted conformable fractional accumulation generating operator. The time power term is introduced into the nonlinear grey Bernoulli model to enhance the flexibility and adaptability of predicting nonlinear and complex sequences. The model parameters are further matrixed so that the interval number sequences can be modeled directly. The improved MPA is chosen to optimize the nonlinear parameters through the algorithm comparison. Finally, the Cramer rule is used to derive the time recursive formula.FindingsThe validity and superiority of the MWCFNGBM(1,1,tα) is verified by the model comparison experiment. The total power generation in East China is predicted and analyzed from 2024 to 2027. The prediction shows that it will grow steadily over the next four years.Originality/valueThe trend of power generation in East China is complex in the short term. It is of research significance to use the grey model for short-term interval prediction of power generation. For the data characteristics of power generation, a grey interval number prediction model for power generation prediction is proposed.