scholarly journals Grey Forecast Model with Aging Fractional Accumulation and Its Properties

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Leping Tu ◽  
Yan Chen ◽  
Lifeng Wu
Keyword(s):  
Prediction Method ◽  
Forecast Model ◽  
Grey Model ◽  
Excellent Performance ◽  
Aging Parameter

A novel aging fractional accumulation operator is proposed. The aging accumulation operator can dynamically update the accumulation weight of data and flexibly change the forecast trend by adjusting the aging parameter. In addition, a new aging accumulated grey model is obtained by using the aging accumulation operator to improve the traditional grey model. In the analysis of four examples, the existing grey accumulation operator and prediction method are compared. The results show that the proposed aging accumulation operator and aging accumulation grey model have excellent performance.

Forecast Model of Phreatic Surface on Tailings Dam Based on GM-GRNN Theory

2010 ◽  
Vol 44-47 ◽  
pp. 3403-3407
Author(s):  
Fei Yue Wang ◽  
Zhi Sheng Xu ◽  
Long Jun Dong
Keyword(s):  
Prediction Method ◽  
Forecast Model ◽  
Random Disturbance ◽  
Tailings Dam ◽  
Phreatic Surface ◽  
Factors Affecting ◽  
Depth Analysis ◽  
Tailing Dam ◽  
Calculation Results ◽  
Surface Prediction

Due to the extremely complicated seepage boundary conditions of tailing dam, the calculation results adopting two-dimensional simplified theory may greatly different from the measured results. It is urgent need of an accurate calculation method to forecast phreatic surface. In-depth analysis of factors affecting tailings dam phreatic surface, phreatic surface prediction model based on GRNN and GM (1,1) was established. A tailing dam engineering is tested using this model. It shows that the model uses the advantages of "accumulative generation" of a Gray prediction method, which weakens the original sequence of random disturbance factors, and increases the regularity of data. It also makes full advantage of the GRNN approximation performance, which has a fast solving speed, describes the nonlinear relationship easily, and avoids the defects of Gray theory.

scholarly journals Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model

2019 ◽  
Vol 11 (21) ◽  
pp. 5921 ◽  
Author(s):  
Peng Zhang ◽  
Xin Ma ◽  
Kun She
Keyword(s):  
Energy Consumption ◽  
Solar Energy ◽  
Nonlinear Coefficient ◽  
Prediction Method ◽  
Decision Makers ◽  
Grey Model ◽  
Correct Prediction ◽  
Grey Prediction ◽  
Real World Datasets ◽  
Application Fields

Energy consumption is an essential basis for formulating energy policy and programming, especially in the transition of energy consumption structure in a country. Correct prediction of energy consumption can provide effective reference data for decision-makers and planners to achieve sustainable energy development. Grey prediction method is one of the most effective approaches to handle the problem with a small amount of historical data. However, there is still room to improve the prediction performance and enlarge the application fields of the traditional grey model. Nonlinear grey action quantity can effectively improve the performance of the grey prediction model. Therefore, this paper proposes a novel incomplete gamma grey model (IGGM) with a nonlinear grey input over time. The grey input of the IGGM model is a revised incomplete gamma function of time in which the nonlinear coefficient determines the performance of the IGGM model. The WOA algorithm is employed to seek for the optimal incomplete coefficient of the IGGM model. Then, the validations of IGGM are performed on four real-world datasets, and the results exhibit that the IGGM model has more advantages than the other state-of-the-art grey models. Finally, the IGGM model is applied to forecast Japan’s solar energy consumption in the next three years.

Research on Data Prediction Methods of Structural Health Monitoring System

2012 ◽  
Vol 256-259 ◽  
pp. 1022-1028
Author(s):  
Chun Xiao ◽  
Xue Ping Hao ◽  
Li Qiao Li ◽  
Wei Li ◽  
Xun Gang Liu
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Model Prediction ◽  
Prediction Method ◽  
Grey Model ◽  
Prediction Methods ◽  
Grey Prediction ◽  
Health Monitoring System ◽  
Term Data

Trend prediction is virtually modeling process for dynamic data. The key to prediction is to establish a model in accordance with actual status, then use the model to predict the trend of object, and infer its behavior in future. Two prediction methods are researched to predict the trend on the observed points of the structure in this paper, which are regression prediction method and grey prediction method. The continuous time strain value of a measured point on Tianxingzhou Yangtze River Bridge is used as data sample for researching. The method of regression analysis is applied for predicting the trend of short-term data, and the method of grey model prediction for predicting long-term data. Regression prediction can assess the health status of the structure and obtain the alarm information effectively by comparing the actual monitoring data with the range of forecast interval. Grey prediction method has great advantages when dealing with poor information. By engineering example this study shows the pros and cons of these two methods, and proves that the method of grey model prediction is more suitable of predicting the trend of object in the structural health monitoring system.

GM (1,1) Model Application in Power Load Forecasting Based on Sliding Average Method

2014 ◽  
Vol 687-691 ◽  
pp. 1300-1303
Author(s):  
Li Zhi Song
Keyword(s):  
Average Method ◽  
Moving Average ◽  
Load Forecasting ◽  
Prediction Method ◽  
Grey Model ◽  
Grey Prediction ◽  
Model Application ◽  
Dispersion Degree ◽  
Power Load ◽  
Power Load Forecasting

The grey prediction method is simple in principle, the sample size was small and simple, suitable for load forecasting.But grey model has some limitations, the data dispersion degree is more bigger,the gray is also more bigger, it will reduce the accuracy of prediction.This paper adopts the moving average method to improve the raw data , so as to increase the data weights, while avoiding predicted value excessive volatility .Through a city of China's power load is instantiated to verify, and Then analyze the results, found that after the GM (1,1) model improved by moving average method can effectively improve the accuracy of load forecasting.

Ground Settlement Prediction Model and its Application for Tunneling Engineering above Mined-Out Regions

2012 ◽  
Vol 524-527 ◽  
pp. 592-597
Author(s):  
Shu Ren Wang ◽  
Hai Qing Zhang
Keyword(s):  
Field Data ◽  
Prediction Method ◽  
High Accuracy ◽  
Measured Data ◽  
Grey Model ◽  
Hyperbolic Model ◽  
Ground Settlement ◽  
Settlement Prediction ◽  
Tunneling Engineering ◽  
Huge Challenge

Although many types of curve fitting methods were used in ground settlement prediction, it is due to every prediction method was not perfect, they have some defects and shortcomings to some extent and ground settlement prediction be up against huge challenge. Usher model, being used for economic and resources prediction, is introduced to ground settlement prediction as a new method, and its mathematics features are also analyzed. After comparative analysis, Origin software is selected for parameters solution of Usher model with an explanation of the solving process. Based on the Shipogou tunnel project which through the mined-out regions in Qingdao-Yinchuan highway, the Usher model for ground settlement is established combining to the field data, of which the parameters are solved with the user-defined function and nonlinear tool of Origin. The predicting results being compared with that of grey model and hyperbolic model, it shows that Usher model is of good adaptability, high accuracy, simple and coinciding well with measured data.

Weak thruster fault prediction method for autonomous underwater vehicles based on grey model

2018 ◽  
Vol 233 (3) ◽  
pp. 348-356
Author(s):  
Weixin Liu ◽  
Mingjun Zhang ◽  
Yujia Wang
Keyword(s):  
Differential Equation ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Prediction Method ◽  
Underwater Vehicles ◽  
Fault Prediction ◽  
Grey Model ◽  
Original Sequence ◽  
Background Value ◽  
Operation Sequence

When adopting the conventional grey model (GM(1,1)) to predict weak thruster fault for autonomous underwater vehicles, the prediction error is not always satisfactory. In order to solve the problem, this article develops a new weak thruster fault prediction method based on an improved GM(1,1). In the developed GM(1,1) based fault prediction method, this article mainly makes improvement in the following aspects: construction of grey background value, solution of whiting differential equation and construction of predicted sequence. Specifically, the integral operation is used in range of the two adjacent steps to obtain the grey background value at first. Second, in the solving of whiting differential equation, the point corresponding to the least difference between the accumulated generation sequence and its predicted sequence is determined, and then this special point’s value in the original sequence is considered as the initial condition of the whiting differential equation. Third, in the construction of predicted sequence, another predicted value is obtained based on the error sequence between the accumulated generating operation sequence and its predicted sequence, and then the new predicted result is used to re-adjust the accumulated generating operation sequence, so as to guarantee the re-adjustability of the fault prediction result. Finally, experiments are performed on Beaver 2 autonomous underwater vehicle to evaluate the prediction performance of the developed method.

scholarly journals Grey Model Optimized by Particle Swarm Optimization for Data Analysis and Application of Multi-Sensors

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2503 ◽  
Author(s):  
Chenming Li ◽  
Hongmin Gao ◽  
Junlin Qiu ◽  
Yao Yang ◽  
Xiaoyu Qu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Prediction Method ◽  
Temperature Data ◽  
Grey Model ◽  
Grey System ◽  
Swarm Optimization ◽  
Pumping Station ◽  
Unit Structure ◽  
Neural Network Prediction

Data on the effective operation of new pumping station is scarce, and the unit structure is complex, as the temperature changes of different parts of the unit are coupled with multiple factors. The multivariable grey system prediction model can effectively predict the multiple parameter change of a nonlinear system model by using a small amount of data, but the value of its q parameters greatly influences the prediction accuracy of the model. Therefore, the particle swarm optimization algorithm is used to optimize the q parameters and the multi-sensor temperature data of a pumping station unit is processed. Then, the change trends of the temperature data are analyzed and predicted. Comparing the results with the unoptimized multi-variable grey model and the BP neural network prediction method trained under insufficient data conditions, it is proved that the relative error of the multi-variable grey model after optimizing the q parameters is smaller.

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