scholarly journals Geometrical Variable Weights Buffer GM(1,1) Model and Its Application in Forecasting of China’s Energy Consumption

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Wei Li ◽  
Han Xie
Keyword(s):  
Energy Consumption ◽  
Maximum Degree ◽  
Original Data ◽  
Grey Model ◽  
Variable Weights ◽  
Proposed Model ◽  
Term Energy ◽  
Long Term Forecasting ◽  
The Stability

In order to improve the application area and the prediction accuracy of GM(1,1) model, a novel Grey model is proposed in this paper. To remedy the defects about the applications of traditional Grey model and buffer operators in medium- and long-term forecasting, a Variable Weights Buffer Grey model is proposed. The proposed model integrates the variable weights buffer operator with the background value optimized GM(1,1) model to implement dynamic preprocessing of original data. Taking the maximum degree of Grey incidence between fitting value and actual value as objective function, then the optimal buffer factor is chosen, which can improve forecasting precision, make forecasting results embodying the internal trend of original data to the maximum extent, and improve the stability of the prediction. To verify the effectiveness of the proposed model, the energy consumption in China from 2002 to 2009 is used for the modeling to forecast the energy consumption in China from 2010 to 2020, and the forecasting results prove that the GVGM(1,1) model has remarkably improved the forecasting ability of medium- and long-term energy consumption in China.

A Mixture Price Trend Model for Long-Term Risk Management

2010 ◽  
pp. 157-173
Author(s):  
Eric S. Fung ◽  
Wai-Ki Ching ◽  
Tak-Kuen Siu
Keyword(s):  
Risk Management ◽  
Financial Risk ◽  
Estimation Method ◽  
Real Data ◽  
Price Trend ◽  
Trend Model ◽  
Proposed Model ◽  
Price Trends ◽  
Long Term Forecasting

In financial forecasting, a long-standing challenging issue is to develop an appropriate model for forecasting long-term risk management of enterprises. In this chapter, using financial markets as an example, we introduce a mixture price trend model for long-term forecasts of financial asset prices with a view to applying it for long-term financial risk management. The key idea of the mixture price trend model is to provide a general and flexible way to incorporate various price trend behaviors and to extract information from price trends for long-term forecasting. Indeed, the mixture price trend model can incorporate model uncertainty in the price trend model, which is a key element for risk management and is overlooked in some of the current literatures. The mixture price trend model also allows the incorporation of users’ subjective views on long-term price trends. An efficient estimation method is introduced. Statistical analysis of the proposed model based on real data will be conducted to illustrate the performance of the model.

Modelling the relationship between population density and air quality using fractional Hausdorff grey multivariate model

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kaihe Shi ◽  
Lifeng Wu
Keyword(s):  
Air Quality ◽  
Population Density ◽  
Composite Index ◽  
Original Data ◽  
Multivariate Model ◽  
Grey Model ◽  
Content Type ◽  
New Information ◽  
Proposed Model ◽  
Comparison Results

Purpose The proposed model can emphasize the priority of new information and can extract messages from the first pair of original data. The comparison results show that the proposed model can improve the traditional grey model. Design/methodology/approach The grey multivariate model with fractional Hausdorff derivative is firstly put forward to enhance the forecasting accuracy of traditional grey model. Findings The proposed model is used to predict the air quality composite index (AQCI) in ten cities respectively. Originality/value The effect of population density on AQCI in cities with poor air quality is not as significant as that of the cities with better air quality.

Multivariate Principal Component Analysis for Production and Energy Consumption of Cutter Suction Dredger

2014 ◽  
Vol 644-650 ◽  
pp. 2211-2215
Author(s):  
Kai Kai Li ◽  
Huan Min Xu
Keyword(s):  
Energy Consumption ◽  
Land Reclamation ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Primary Component ◽  
Contribution Rate ◽  
Dimensional Reduction Method ◽  
The Stability ◽  
And Control

Cutter suction dredgers perform a major part in the field of dredging engineering in harbors, fairways, and land reclamation. However, there are many parameters in cutter suction dredger operation so that it is difficult to guarantee the stability of production. In consideration of the issue of enormous parameters in dredging operation, mathematical dimensional reduction method which uses multivariate primary component analysis is proposed. The method can calculate the contribution rate and cumulative contribution rate of each parameter and then select the principal components which influents the production and energy consumption. These parameters represent the majority of the original data information, while not interrelated with each other. The primary components can be used to guide the regulation and control of the parameters, reduce regulatory parameters and operational complexity and provide a theoretical basis for intelligent automation of dredging operations.

scholarly journals Research on the Stability of NDGM Model with the Fractional Order Accumulation and Its Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Huiming Duan ◽  
Kailiang Shao ◽  
Xinping Xiao ◽  
Jinwei Yang
Keyword(s):  
Fractional Order ◽  
Estimation Method ◽  
Original Data ◽  
Grey Model ◽  
Prediction Errors ◽  
Matrix Perturbation ◽  
Perturbation Bound ◽  
Grey Forecasting ◽  
The Matrix ◽  
The Stability

The grey forecasting model has been successfully applied in numerous fields since it was proposed. The nonhomogeneous discrete grey model (NDGM) was approximately constructed based on the nonhomogeneous index trend; it increased the applicability of discrete grey model. However, the NDGM required accurate data and better effect when the original data did not meet the conditions and fitting and prediction errors were larger. For this, the NDGM with the fractional order accumulating operator (abbreviated as NDGMp/q) has higher performance. In this paper, the matrix perturbation bound of the parameters was used to analyze the stability of NDGMp/q and the NDGMp/q can decrease the disturbance bound. Subsequently, the parameter estimation method of NDGMp/q was studied and the Particle Swarm Optimization algorithm was employed to optimize the order number of NDGMp/q and some steps were provided. In addition, the results of two practical examples demonstrated that the perturbation of NDGMp/q was smaller than that of NDGM and provided remarkable predication performance compared with the traditional NDGM model and DGM model.

Mechanism of bolt pretightening and preload relaxation in composite interference-fit joints under thermal effects

2020 ◽  
Vol 54 (30) ◽  
pp. 4929-4946
Author(s):  
Junshan Hu ◽  
Kaifu Zhang ◽  
Hui Cheng ◽  
Zhenchao Qi
Keyword(s):  
Thermal Effects ◽  
Viscoelastic Model ◽  
Delayed Response ◽  
Elastic Model ◽  
Interference Fit ◽  
Linear Elastic ◽  
Proposed Model ◽  
The Stability ◽  
Long Term Behavior

The present research investigates the mechanism of bolt pretightening and preload relaxation in composite interference-fit joint structures under thermal effects. In view of the dissimilar material properties and the interfacial friction in such joints, the mechanical behavior of bolt and composites during assembly can be regarded as immediate preload response which is described by a linear elastic model, whereas the long-term behavior of composites during preload relaxation in service is considered as delayed response which is characterized by a viscoelastic model. The clamping forces on both sides of joints were captured to evaluate the preload balanced by the frictional behavior. The preload relaxation of joints with various interference-fit sizes and tightening torques under thermal effects were monitored for 240 hours to calibrate and validate the proposed model. The research revealed that the interference-fit size determined the level of frictional force which resulted in a difference between clamping forces at two sides of the fasteners. The preload first increases slowly with the growth of temperature, then decreases sharply when it approaches to glass transition temperature of matrix. The interference-fit joint behaves better in terms of maintaining the stability of preload than clearance-fit joints.

scholarly journals DETAIL SURVEY OF LONG-TERM ENERGY CONSUMPTION FOR 80 HOUSES IN PRINCIPAL CITYIES OF JAPAN : Description of the houses and end use structure of annual energy consumption

2006 ◽  
Vol 71 (603) ◽  
pp. 93-100 ◽  
Author(s):  
Shuzo MURAKAMI ◽  
Kazuaki BOGAKI ◽  
Toshihiko TANAKA ◽  
Hirofumi HAYAMA ◽  
Hiroshi YOSHINO ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Term Energy ◽  
End Use

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.

Sign in / Sign up

Export Citation Format

Share Document