scholarly journals Multivariate Grey Prediction Model with Priority Accumulation of New Information

2021 ◽  
Vol 248 ◽  
pp. 02009
Author(s):  
Lanxi Zhang
Keyword(s):  
Prediction Model ◽  
Absolute Error ◽  
Good Effect ◽  
Grey Prediction ◽  
Grey System ◽  
Engineering Project ◽  
Average Percentage ◽  
Grey Prediction Model ◽  
New Information ◽  
Proposed Model

According to the new information priority principle of grey system, this paper tries to optimize the traditional multivariate grey prediction model. Firstly, the basic theory of the traditional grey prediction model is put forward. Based on this, the background value is improved by using the new information priority principle, and the cumulative generation with parameters is defined. Taking the settlement trend of A4# building of an engineering project in Anhui province as an example, the model is applied to the settlement analysis, and the proposed model is compared with the existing grey prediction model, the average percentage absolute error between the predicted value and the observed value is calculated, and the regression graphs of each model are drawn. Through the analysis, we can see that the established model has achieved a good effect, and then verified the practicability and reliability of the proposed model.

scholarly journals A Hybrid Grey Prediction Model for Small Oscillation Sequence Based on Information Decomposition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Meng Zhou ◽  
Bo Zeng ◽  
Wenhao Zhou
Keyword(s):  
Prediction Model ◽  
Small Oscillation ◽  
New Method ◽  
Superior Performance ◽  
Small Data ◽  
Grey Prediction ◽  
New Model ◽  
Grey Prediction Model ◽  
Proposed Model ◽  
Oscillation Characteristic

Grey prediction model has good performance in solving small data problem, and has been widely used in various research fields. However, when the data show oscillation characteristic, the effect of grey prediction model performs poor. To this end, a new method was proposed to solve the problem of modelling small data oscillation sequence with grey prediction model. Based on the idea of information decomposition, the new method employed grey prediction model to capture the trend characteristic of complex system, and ARMA model was applied to describe the random oscillation characteristic of the system. Crops disaster area in China was selected as a case study and the relevant historical eight-year data published by government department were substituted to the proposed model. The modelling results of the new model were compared with those of other traditional mainstream prediction models. The results showed that the new model had evidently superior performance. It indicated that the proposed model will contribute to solve small oscillation problems and have positive significance for improving the applicability of grey prediction model.

scholarly journals Application of a Fractional Grey Prediction Model Based on a Filtering Algorithm in Image Processing

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Mingyu Tong ◽  
Kailiang Shao ◽  
Xilin Luo ◽  
Huiming Duan
Keyword(s):  
Image Processing ◽  
Prediction Model ◽  
Grey System Theory ◽  
Grey Prediction ◽  
Grey System ◽  
Middle Point ◽  
Filtering Algorithm ◽  
Grey Correlation ◽  
Grey Prediction Model ◽  
Model Based

Image filtering can change or enhance an image by emphasizing or removing certain features of the image. An image is a system in which some information is known and some information is unknown. Grey system theory is an important method for dealing with this kind of system, and grey correlation analysis and grey prediction modeling are important components of this method. In this paper, a fractional grey prediction model based on a filtering algorithm by combining a grey correlation model and a fractional prediction model is proposed. In this model, first, noise points are identified by comparing the grey correlation and the threshold value of each pixel in the filter window, and then, through the resolution coefficient of the important factor in image processing, a variety of grey correlation methods are compared. Second, the image noise points are used as the original sequence by the filter pane. The grey level of the middle point is predicted by the values of the surrounding pixel points combined with the fractional prediction model, replacing the original noise value to effectively eliminate the noise. Finally, an empirical analysis shows that the PSNR and MSE of the new model are approximately 27 and 140, respectively; these values are better than those of the comparison models and achieve good processing effects.

scholarly journals A Multivariate Grey Prediction Model Using Neural Networks with Application to Carbon Dioxide Emissions Forecasting

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yu-Jing Chiu ◽  
Yi-Chung Hu ◽  
Peng Jiang ◽  
Jingci Xie ◽  
Yen-Wei Ken
Keyword(s):  
Carbon Dioxide ◽  
Prediction Model ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Proposed Model ◽  
Predicted Values ◽  
Carbon Dioxide Co2 ◽  
Grey Relational

The forecast of carbon dioxide (CO2) emissions has played a significant role in drawing up energy development policies for individual countries. Since data about CO2 emissions are often limited and do not conform to the usual statistical assumptions, this study attempts to develop a novel multivariate grey prediction model (MGPM) for CO2 emissions. Compared with other MGPMs, the proposed model has several distinctive features. First, both feature selection and residual modification are considered to improve prediction accuracy. For the former, grey relational analysis is used to filter out the irrelevant features that have weaker relevance with CO2 emissions. For the latter, predicted values obtained from the proposed MGPM are further adjusted by establishing a neural-network-based residual model. Prediction accuracies of the proposed MGPM were verified using real CO2 emission cases. Experimental results demonstrated that the proposed MGPM performed well compared with other MGPMs considered.

A novel adaptive discrete grey prediction model for forecasting development in energy consumption structure—from the perspective of compositional data

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wuyong Qian ◽  
Hao Zhang ◽  
Aodi Sui ◽  
Yuhong Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Compositional Data ◽  
Development Trend ◽  
Grey Prediction ◽  
Content Type ◽  
Consumption Structure ◽  
Grey Prediction Model ◽  
Proposed Model

PurposeThe purpose of this study is to make a prediction of China's energy consumption structure from the perspective of compositional data and construct a novel grey model for forecasting compositional data.Design/methodology/approachDue to the existing grey prediction model based on compositional data cannot effectively excavate the evolution law of correlation dimension sequence of compositional data. Thus, the adaptive discrete grey prediction model with innovation term based on compositional data is proposed to forecast the integral structure of China's energy consumption. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of China's energy consumption structure is conducted into a future horizon from 2021 to 2035 by using the model.FindingsChina's energy structure will change significantly in the medium and long term and China's energy consumption structure can reach the long-term goal. Besides, the proposed model can better mine and predict the development trend of single time series after the transformation of compositional data.Originality/valueThe paper considers the dynamic change of grey action quantity, the characteristics of compositional data and the impact of new information about the system itself on the current system development trend and proposes a novel adaptive discrete grey prediction model with innovation term based on compositional data, which fills the gap in previous studies.

Weapon equipment management cost prediction based on forgetting factor recursive GM (1,1) model

2019 ◽  
Vol 10 (1) ◽  
pp. 38-45
Author(s):  
Subing Liu ◽  
Yin Chunwu ◽  
Cao Dazhi
Keyword(s):  
Prediction Model ◽  
Recursive Estimation ◽  
Model Parameters ◽  
Grey Prediction ◽  
Forgetting Factor ◽  
Content Type ◽  
Grey Prediction Model ◽  
Model Based ◽  
New Information ◽  
Equipment System

Purpose The purpose of this paper is to provide a new recursive GM (1,1) model based on forgetting factor and apply it to the modern weapon and equipment system. Design/methodology/approach In order to distinguish the contribution of new and old data to the grey prediction model with new information, the authors add forgetting factor to the objective function. The purpose of the above is to realize the dynamic weighting of new and old modeling data, and to gradually forget the old information. Second, the recursive estimation algorithm of grey prediction model parameters is given, and the new information is added in real time to improve the prediction accuracy of the model. Findings It is shown that the recursive GM (1,1) model based on forgetting factor can achieve both high effectiveness and high efficiency. Originality/value The paper succeeds in proposing a recursive GM (1,1) model based on forgetting factor, which has high accuracy. The model is applied to the field of modern weapon and equipment system and the result the model is better than the GM(1,1) model. The experimental results show the effectiveness and the efficiency of the prosed method.

scholarly journals A Nonhomogeneous Multivariable Grey Prediction NMGM Modeling Mechanism and Its Application

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Haixia Wang ◽  
Lingdi Zhao
Keyword(s):  
Prediction Model ◽  
Least Square Method ◽  
Original Data ◽  
Least Square ◽  
The Novel ◽  
Grey Prediction ◽  
Prediction Theory ◽  
Grey Prediction Model ◽  
Index Data ◽  
Proposed Model

The purpose of this paper is to explore modeling mechanism of a nonhomogeneous multivariable grey prediction NMGM(1, m, kα) model and its application. Although multi-variable grey prediction MGM(1, m) model has been employed in many fields, its prediction results are not always satisfactory. Traditional MGM(1, m) model is constructed on the hypothesis that original data sequences are in accord with homogeneous index trend; however, the nonhomogeneous index data sequences are the most common data existing in all systems, and how to handle multivariable nonhomogeneous index data sequences is an urgent problem. This paper proposes a novel nonhomogeneous multivariable grey prediction model termed NMGM(1, m, kα) to deal with those data sequences that are not in accord with homogeneous index trend. Based on grey prediction theory, by least square method and solutions of differential equations, the modeling mechanism and time response function of the proposed model are expounded. A case study demonstrates that the novel model provides preferable prediction performance compared with traditional MGM(1, m) model. This work is an extension of the multivariable grey prediction model and enriches the study of grey prediction theory.

scholarly journals Forecasting Oil Consumption with Novel Fractional Grey Prediction Model Based on Simpson Formula

2019 ◽  
pp. 1-27
Author(s):  
Xiwang Xiang ◽  
Peng Zhang ◽  
Lang Yu
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Model Error ◽  
Grey System Theory ◽  
Human Society ◽  
Grey Prediction ◽  
Oil Consumption ◽  
Grey System ◽  
Grey Prediction Model ◽  
Grey Models

With the development of human society, the evolving transition of energy will become a common challenge that mankind has to face together. In this context, it is crucial to make scientific and reasonable predictions about energy consumption. This paper presents a novel fractional grey prediction model FGM(1,1,k2) based on the classical fractional grey system theory. In order to improve the prediction accuracy of the FGM(1,1,k2) model, we further analyze the model error and propose improved grey model called as SFGM with optimization of background value. The numerical cases point out that SFGM(1,1,k2) significantly outperforms other existing fractional grey models. Finally, the proposed SFGM(1,1,k2) is applied to the forecasting of oil consumption, the predicted results would provide a reference for making energy policy in new situations.

scholarly journals Output Temperature Predictions of the Geothermal Heat Pump System Using an Improved Grey Prediction Model

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5075
Author(s):  
Khaled Salhein ◽  
Javed Ashraf ◽  
Mohamed Zohdy
Keyword(s):  
Prediction Model ◽  
Power Efficiency ◽  
Grey Prediction ◽  
Geothermal Heat ◽  
Pump System ◽  
Heat Pump System ◽  
Grey Prediction Model ◽  
Proposed Model ◽  
Out Of Sample ◽  
Improved Model

This paper presents the Improved Grey Prediction Model, also called IGM (1,1) model, to increase the prediction accuracy of the Grey Prediction Model (GM) model that performs the GHPS output temperature prediction. This was based on correcting the current predicted value by subtracting the error between the previous predicted value and the previous immediate mean of the measured value. Subsequently, the IGM (1,1) model was applied to predict the output temperature of the GHPSs at Oklahoma University, the University Politècnica de València, and Oakland University, respectively. For each GHPS, the model uses a small dataset of 24 data points (i.e., 24 h) for training to predict the output temperature eight hours in advance. The proposed model was verified using three different output temperature datasets; these datasets were also used to validate the power efficiency of the proposed model. In addition, the empirical results show that the proposed IGM (1,1) model significantly improves the simulation (in-sample) and the prediction (out-of-sample) of the output temperature of the GHPS through error reduction, thereby enhancing the GM (1,1) model’s overall accuracy. As a result, the prediction accuracies were compared, and the improved model was found to be more accurate than the GM (1,1) model in both simulation and prediction results for all datasets used.

Application of Grey Prediction Model in Electric Power Consumption in Shandong Province

2012 ◽  
Vol 524-527 ◽  
pp. 3021-3026
Author(s):  
Ke Ying Zhang ◽  
Jing Zhao ◽  
Yang Dong Li
Keyword(s):  
Power Consumption ◽  
Prediction Model ◽  
Electric Power ◽  
Forecast Accuracy ◽  
Reference Value ◽  
Shandong Province ◽  
Electric Power Consumption ◽  
Grey Prediction ◽  
Grey System ◽  
Grey Prediction Model

This paper takes Shandong Province as an example, by using grey system forecast theory; the GM (1, 1) model of electric power consumption is established. The electric power consumption from 2011 to 2015 in Shandong Province is forecasted and the forecast accuracy is verified, which offers the reference value for Shandong Province electric power planning in the future.

scholarly journals Application of Grey Prediction in a GA-BP Power Theft Algorithm

2020 ◽  
Author(s):  
Xiaofeng Chen ◽  
Zhongping Xu ◽  
Lipeng Zhang ◽  
Feng Zhu ◽  
Xiaoming Qi ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Power Grid ◽  
Absolute Error ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Line Loss ◽  
Predicted Values

Statistics show that power theft is one of the main reasons for the dramatic increase in power grid line loss. In this paper, a genetic algorithm is used to optimize a neural network and establish a power theft prediction model. With the grey prediction model, the predicted values of variables are obtained and then applied to the prediction model of a GA-BP neural network to obtain relatively accurate predictions from limited samples, reducing the absolute error. Through the two levels of prediction and analysis, the model is demonstrated to have good universality in predicting power theft behavior, and is a practical and effective method for power companies to carry out power theft analysis.

Sign in / Sign up

Export Citation Format

Share Document