Lithium-ion battery remaining useful life prediction based on grey support vector machines

In the era of big data, the value of data has received unprecedented attention. Predictive analysis is an important direction of data application. Different data are suitable for different prediction models, and the prediction accuracy is different. In this paper, in order to accurately find a variety of data to adapt to the prediction model, the data of three different areas of high school enrollment in Shaanxi, Xi’an and China were predicted and analyzed. The results of polynomial fitting, grey model and grey prediction model based on wavelet transform are compared. After demonstration and analysis, the polynomial fitting is more effective to fill missing data. Grey prediction model and grey combination model based on wavelet transform are suitable for data prediction, and the grey combination model based on wavelet transform is more accurate than grey prediction model, and it is more suitable for the predictive analysis of enrollment number. The predicted results can provide reference and basis for the educational administrative departments to make decisions.

Grey extended prediction model based on IRLS and its application on smog pollution

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

A fluctuation data grey model and its prediction of rainstorm days

Purpose Seasonal data are characterized by multiple complex features such as seasonal fluctuations and stage differences, which presents challenges for the scientifically sound construction of its predictive models. This article proposes a novel seasonal grey prediction model with a weighted fractional order accumulation operator for accurate prediction of China’s natural gas production. The grey models proposed in this article optimize the accumulation method of the traditional grey model, and flexibly adjust the generation sequence through the two parameters introduced, so as to explore the internal law of the data information at a deeper level, and achieve the purpose of improving the model accuracy. Design/methodology/approach Firstly, based on the seasonal fluctuations of the raw data, the raw data is divided into four seasonal groups. Secondly, when an external disturbance affects the system, the classic average weakening buffer operator is used to weaken its effects. Then, the new information accumulation parameters ? and the fractional-order cumulative generating operator parameter r, are optimized using the particle swarm optimization (PSO) technique. Findings The experimental results show that the new grey prediction model (DGGM(1,1,λ,r)and DGGM(1,N,λ,r)) perform better than other models. This model is more suitable for solving the prediction problem of seasonal fluctuation systems with external disturbances. Practical implications The seasonal grey model proposed in this article is suitable for predicting data sequences with seasonal changes while considering the impact of external shock. Two models proposed in this article are used in two cases, i.e. estimating China’s natural gas production and predicting total electricity consumption in Nanjing city. Accurately predicting to provide a decision-making basis for the Chinese government to formulate energy policies, plan energy layouts, and adjust industrial structures. Originality/value Simultaneously considering seasonal changes and external shock disturbances, the data grouping approach is used to identify seasonal changes. This article proposes a new weighted fractional order accumulation operator that combines the new information accumulation operator and the fractional order accumulation operator. A novel seasonal grey prediction model with external impacts is proposed to improve the predictive performance of the system. The PSO algorithm is used to solve the parameter optimization problem of the model.

A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecasting

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.

On unified framework for continuous-time grey models: An integral matching perspective

Forecast of biofuel production and consumption in top CO2 emitting countries using a novel grey model

In this paper, three novel grey breakpoint prediction models are proposed based on calculating the development coefficient and grey action of grey prediction models after fuzzy breakpoints, unifying the calculation methods for parameter estimation and the relevant time-response equations, and using the particle swarm optimisation algorithm to optimise the two-stage background values. Finally, the novel grey breakpoint prediction models are used to simulate and forecast the CO2 emissions in BRICS countries. We can see that by setting time breakpoints and fuzzy breakpoint intervals, the novel methods successfully detect abrupt changes in the system and achieve accurate predictions, thus improving the accuracy and applicability of the grey model. The new grey breakpoint prediction models demonstrate better estimation in all cases in CO2 emissions forecasting. The projections show that between 2022 and 2025, CO2 emissions in Brazil and South Africa will decrease each year, while CO2 emissions in China, Russia and India will increase each year, but the upwards trend in India shows signs of slowing.

As the number of college students and graduate students increasing, taking part in the entrance exams for postgraduate schools has become more and more popular for college students' choice. The increasing number of taking part in the entrance exams for postgraduate schools has the vital significance to our economic construction and education development. By application of grey system theory and its methods, based on the change in the number of taking part in the entrance exams for postgraduate schools in recent years in China, this article constructed grey model GM(1,1) and grey metabolic prediction model, compared two models, and took some relative optimum processing. At last, using grey metabolic prediction model to forecast the number of students who take part in the entrance exams for postgraduate schools in next five years, provided a reference for the relevant departments.

A new prediction model presented in this paper is called the grey model with convolution integral, abbreviated as GMC(1, n). Additional n − 1 series can be introduced by the new model GMC(1, n) to improve the accuracy of prediction made by the existing grey model GM(1, 1). Improvements on the existing grey prediction model GM(1, n) are made to a large extent and the messages for a system can be inserted sufficiently. The relationship between input series and output series is constructed sufficiently by GMC(1, n) and high accuracy of indirect measurement or prediction for a dynamic system can be broadly anticipated. The relationship between the tensile strength and Brinell hardness number of a material is very intensive, but a definite explicit or implicit function to describe it does not exist. The property of a material is a grey system. It is more difficult to measure the tensile strength than the Brinell hardness number of a material especially for a higher temperature. The indirect measurement of the tensile strength of a material for a higher temperature can be made by GMC(1, n) with the Brinell hardness number of the material acting as a leading indicator. The results show that the accuracy of the indirect measurement of the tensile strength is satisfactory.

A novel grey-fuzzy regulator using genetic algorithms for a dc motor is presented in this paper. The grey system theory is employed to design a grey prediction model. After the grey model predicting the feedback errors of the motor speed, a compensated energy will be generated for the fuzzy controller. Appending the grey predictor to the feedback control system, then the fuzzy inference engine receives not only the current data but also the future information. Furthermore, the optimal parameter of the grey model is acquired via genetic algorithms. Computer simulation results show that the regulation performance of the motor speed and the control energy is improved.

Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease. However, the results obtained from the predictive models are imprecise and inefficient because the dataset contains linear and non-linear patterns, respectively. Linear models such as autoregressive integrated moving average cannot be used effectively to predict complex time series, so nonlinear approaches are better suited for such a purpose. Therefore, to achieve a more accurate and efficient predictive value of COVID-19 that is closer to the true value of COVID-19, a hybrid approach was implemented. Therefore, the objectives of this study are twofold. The first objective is to propose intelligence-based prediction methods to achieve better prediction results called autoregressive integrated moving average-least-squares support vector machine. The second objective is to investigate the performance of these proposed models by comparing them with the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine. Our investigation is based on three COVID-19 real datasets, i.e., daily new cases data, daily new death cases data, and daily new recovered cases data. Then, statistical measures such as mean square error, root mean square error, mean absolute error, and mean absolute percentage error were performed to verify that the proposed models are better than the autoregressive integrated moving average, support vector machine model, least-squares support vector machine, and autoregressive integrated moving average-support vector machine. Empirical results using three recent datasets of known the Coronavirus Disease-19 cases in Malaysia show that the proposed model generates the smallest mean square error, root mean square error, mean absolute error, and mean absolute percentage error values for training and testing datasets compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine models. This means that the predicted value of the proposed model is closer to the true value. These results demonstrate that the proposed model can generate estimates more accurately and efficiently. Compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average-support vector machine models, our proposed models perform much better in terms of percent error reduction for both training and testing all datasets. Therefore, the proposed model is possibly the most efficient and effective way to improve prediction for future pandemic performance with a higher level of accuracy and efficiency.

A novel nonhomogeneous multivariable grey forecasting model termed NHMGM(1,m,kp,c) is proposed in this paper for use in nonhomogeneous multivariable exponential data sequences. The NHMGM(1,m,kp,c) model is able to reflect the nonlinear relation of the data sequences in the system, and it is proved that many classic grey forecasting models can be derived from NHMGM(1,m,kp,c) model. Parameters of the novel model are obtained by using least square method, and the time response function is given. A numerical example is presented to show the effectiveness of the proposed model, six different grey forecasting models are built for modeling, and two popular accuracy criteria (ARPE and MAPE) are adopted to test the reliability of the novel model. The example demonstrates that NHMGM-2 model provides favorable performance compared with the other five grey models. Additionally, the multiplication transformation properties of NHMGM(1,m,kp,c) are systematically analysed, which establish a theoretical foundation for further applications of the model.