An accumulative time-delay multivariate grey model with nonlinear structure and its application

The novel fractional discrete multivariate grey system model and its applications

A novel time-delay multivariable grey model and its application in predicting oil production

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Open access article.

Short-term electricity load forecasting is one of the most important operations in electricity markets. The success in the operations of electricity market participants partially depends on the accuracy of load forecasts. In this paper, three grey prediction models, which are seasonal grey model (SGM), multivariable grey model (GM (1,N)) and genetic algorithm based multivariable grey model (GAGM (1,N)), are proposed for short-term load forecasting problem in day-ahead market. The effectiveness of these models is illustrated with two real-world data sets. Numerical results show that the genetic algorithm based multivariable grey model (GAGM (1,N)) is the most efficient grey forecasting model through its better forecast accuracy.

The corrosion of steel bars in concrete has a significant impact on the durability of constructed structures. Based on the gray relational analysis (GRA) of the accelerated corrosion data and practical engineering data using MATLAB, a back propagation neural network (BPNN) model, a multivariable gray prediction model (GM (1, N)), and an optimization multivariable gray prediction model (OGM (1, N)) of steel corrosion were established by using a sequence of the key affecting factors. By comparing the prediction results of the three models, it is found that the GM (1, N) model has larger fitting and prediction errors for steel corrosion, while the OGM (1, N) model has smaller prediction errors in the accelerated corrosion data; the BPNN model offers more accurate predictions of the practical engineering data. The results show that the BPNN and OGM (1, N) models are all suitable for the prediction of steel bar corrosion in concrete structures.

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

Purpose – The purpose of this paper is to solve the problem that the qualitative relative factors cannot be employed in traditional multivariate grey models. Design/methodology/approach – First, a new model is constructed though introducing dummy drivers. Then, the parameters estimation method and recursive function of the model are discussed. Furthermore, dummy driver setting, pre and post test methods of dummy drivers are proposed. At last, the per capita income forecasting of rural residents in Henan province of China is solved with the proposed model. Findings – The proposed model is the reasonable extension of original one. The accuracy of it is higher than former model. In the case study, the forecasting results of proposed model are compared with other grey forecasting models, and prove that proposed model has not only high accuracy, but also clear physical meaning. Practical implications – The method proposed in the paper could be used in policy effect measure, marketing forecasting, etc., when the predictor variables are influenced by some qualitative variables. Originality/value – It will promote the accuracy of multivariate grey forecasting model.

The intensified contradiction between water resources and social development has restricted the development of the Yangtze River Delta. Due to the importance of water consumption in relieving this contradiction, this paper proposes a novel cumulative multivariable grey model with a high performance to predict the water consumption. Firstly, the grey correlation analysis is applied to study the influencing factors, and then the DGM(1,N) with deformable accumulation (DDGM(1,N) model) is constructed and used to predict the water consumption. The results show that the resident population has a significant impact on the water consumption, and the performance of the DDGM(1,N) model is better than the other two grey models. Secondly, the proposed novel grey model is applied to predict the water consumption in 17 cities in the Yangtze River Delta, and the predicted water consumption in Zhejiang and Shanghai indicates a downward trend, while the predicated water consumption in some cities of the Anhui Province presents an upward trend, such as Chizhou, Chuzhou, Wuhu and Tongling. Finally, some policy implications are provided that correspond to the population growth and three major industries in different situations. This paper enriches the research method and prediction analysis used for the water consumption, and the findings can provide some decision-making references for water resources management.

PurposeHigh-tech industries play an important role in promoting economic and social development. The purpose of this paper is to accurately predict and analyze the output value of high-tech products in Guangdong Province, China, by using a multivariable grey model.Design/methodology/approachBased on the principle of fractional order accumulation, this study proposes a multivariable grey prediction model. To further enhance the prediction ability and accuracy of the model, an optimized model is established by reconstructing the background value. The optimal parameters are solved by minimizing the average relative error of the system characteristic sequence with the constraint of parameter relationships.FindingsThe results from the study show that the two proposed models exhibit better simulation and prediction performance than the traditional models, while the optimized model can significantly improve the modelling precision. In addition, it is predicted that the output value of high-tech products is 12,269.443bn yuan in 2021, which will approximately double from 2016 to 2021.Research limitations/implicationsThe two proposed models can be used to forecast the trend of the system and are grown as an effective extension and supplement of the traditional multivariable grey forecasting models.Practical implicationsThe forecast and analysis of the development prospects of high-tech industries would be useful for the government departments of Guangdong Province and professional forecasters to grasp the future of high-tech industries and formulate decision planning.Originality/valueA new multivariable grey prediction model based on fractional order accumulation and its optimized model obtained by reconstructing the background value, which can improve the modelling accuracy of the traditional model, is proposed in this paper.

The grey multi-variable convolution model (GMC(1, N)) is a quality improvement of the traditional grey multi-variable prediction model (GM(1, N)) and has been successfully applied in many practical problems. However, the GMC(1, N) model still has some defects in several aspects; for instance, parameter estimation, simple model structure, and so on. To further improve the prediction accuracy and enhance the stability of the GMC(1, N) model, an optimized discrete GMC(1, N) model (ODGMC(1, N)) is proposed in this paper. In particular, a linear correction item is introduced in the new model, the parameters are computed consistently with the modeling process and the time response function of the new model is simply derived by the recursive method. The new proposed ODGMC(1,N) model not only can adjust the relationships between dependent variables and independent variables, but also show better stability than the GMC(1, N) model and its discrete form. Three numerical examples from different application fields are presented to confirm our findings. Numerical results show that the proposed ODGMC(1, N) model has both better fitting accuracy and prediction accuracy than the traditional GM(1, N) model, the GMC(1, N) model and their discrete forms, whether the sequence of dependent variable is increasing, deceasing, or fluctuating.

An enhanced multivariable dynamic time-delay discrete grey forecasting model for predicting China's carbon emissions

Fractional order time-delay multivariable discrete grey model for short-term online public opinion prediction

Time-delay nonlinear model based on interval grey number and its application

Depression and Incident Alzheimer Disease: The Impact of Disease Severity

Development of an optimization method for the GM(1,N) model