scholarly journals On Novel Nonhomogeneous Multivariable Grey Forecasting Model NHMGM

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Haixia Wang ◽  
Peiguang Wang ◽  
M. Tamer Şenel ◽  
Tongxing Li
Keyword(s):  
Least Square Method ◽  
Least Square ◽  
Forecasting Model ◽  
Model Parameters ◽  
The Novel ◽  
Forecasting Models ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Nonlinear Relation ◽  
Novel Model

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.

scholarly journals Forecasting Carbon Dioxide Emissions for Singapore using Grey Model with Cramer’s Rule

2021 ◽  
Vol 17 (4) ◽  
pp. 437-445
Author(s):  
Assif Shamim Mustaffa Sulaiman ◽  
Ani Shabri
Keyword(s):  
Carbon Dioxide ◽  
Global Warming ◽  
Carbon Dioxide Emissions ◽  
Grey Model ◽  
Forecasting Model ◽  
Fossil Carbon ◽  
Forecasting Models ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Cramer’S Rule

This article analyses and forecasts carbon dioxide () emissions in Singapore for the 2012 to 2016 period. The study analysed the data using grey forecasting model with Cramer’s rule to calculate the best SOGM(2,1) model with the highest accuracy of precision compared to conventional grey forecasting model. According to the forecasted result, the fitted values using SOGM(2,1) model has a higher accuracy precision with better capability in handling information to fit larger scale of uncertain feature compared to other conventional grey forecasting models. This article offers insightful information to policymakers in Singapore to develop better renewable energy instruments to combat the greater issues of global warming and reducing the fossil carbon dioxide emissions into the environment.

Study of a discrete grey forecasting model based on the quality cost characteristic curve

2017 ◽  
Vol 7 (3) ◽  
pp. 376-384 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Xiaoyu Yang ◽  
Qian Hu ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Characteristic Curve ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Quality Level ◽  
Cost Models ◽  
Quality Cost ◽  
Content Type ◽  
Grey Forecasting ◽  
Grey Forecasting Model

Purpose The purpose of this paper is to clarify several commonly used quality cost models based on Juran’s characteristic curve. Through mathematical deduction, the lowest point of quality cost and the lowest level of quality level (often depicted by qualification rate) can be obtained. This paper also aims to introduce a new prediction model, namely discrete grey model (DGM), to forecast the changing trend of quality cost. Design/methodology/approach This paper comes to the conclusion by means of mathematical deduction. To make it more clear, the authors get the lowest quality level and the lowest quality cost by taking the derivative of the equation of quality cost and quality level. By introducing the weakening buffer operator, the authors can significantly improve the prediction accuracy of DGM. Findings This paper demonstrates that DGM can be used to forecast quality cost based on Juran’s cost characteristic curve, especially when the authors do not have much information or the sample capacity is rather small. When operated by practical weakening buffer operator, the randomness of time series can be obviously weakened and the prediction accuracy can be significantly improved. Practical implications This paper uses a real case from a literature to verify the validity of discrete grey forecasting model, getting the conclusion that there is a certain degree of feasibility and rationality of DGM to forecast the variation tendency of quality cost. Originality/value This paper perfects the theory of quality cost based on Juran’s characteristic curve and expands the scope of application of grey system theory.

Examination of Correlation Between Arousal Level and Auditory Environmental Change Using Rock and Classic Music

2017 ◽  
Author(s):  
Kentaro Miyago ◽  
Kenyu Uehara ◽  
Takashi Saito
Keyword(s):  
Visual Cortex ◽  
Frontal Lobe ◽  
Traffic Accidents ◽  
Evaluation Method ◽  
Duffing Oscillator ◽  
Least Square Method ◽  
Arousal Level ◽  
Least Square ◽  
Model Parameters ◽  
Eeg Data

Recently, traffic accidents due to drowsy driving, operation mistake in the power plant by drowsiness and decrease arousal in employment during work have been attracted as problems. To avoid such an accident, arousal level could be quantitatively evaluated in real time. We suggested that the one of the parameters of Duffing oscillator parameters is related to the conventional arousal level using the EEG frequency component. However, in this examination, effects on the EEG from visual and active behavior were considered, but those from hearing also need to be investigated. In this paper, we performed the experiment in the musical environment using rock and classic music to investigate the model parameters for effect of the auditory stimulation, and acquired EEG data in Visual cortex and Frontal lobe. The acquired EEG data was used to identify the model parameters, which were identified solving the inverse problem by Least Square method. Results of investigating correlation between conventional arousal revel and model parameter shows a significant correlation in case of the auditory environmental situation. Moreover, Visual cortex is better than Frontal lobe as a measurement point in this evaluation method.

A comparative study of univariate models for container throughput forecasting of major ports in Asia

2021 ◽  
pp. 147509022110236
Author(s):  
Juan Huang ◽  
Ching-Wu Chu ◽  
Hsiu-Li Hsu
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Forecasting Model ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Port Authorities ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Short Term Forecasting ◽  
Trigonometric Regression

This study aims to make comparisons on different univariate forecasting methods and provides a more accurate short-term forecasting model on the container throughput for rendering a reference to relevant authorities. We collected monthly data regarding container throughput volumes for three major ports in Asia, Shanghai, Singapore, and Busan Ports. Six different univariate methods, including the grey forecasting model, the hybrid grey forecasting model, the multiplicative decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, and the seasonal autoregressive integrated moving average (SARIMA) model, were used. We found that the hybrid grey forecasting model outperforms the other univariate models. This study’s findings can provide a more accurate short-term forecasting model for container throughput to create a reference for port authorities.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

Vulnerability of hydropower generation to climate change in China: Results based on Grey forecasting model

Energy Policy ◽  
2014 ◽  
Vol 65 ◽  
pp. 701-707 ◽  
Author(s):  
Bing Wang ◽  
Xiao-Jie Liang ◽  
Hao Zhang ◽  
Lu Wang ◽  
Yi-Ming Wei
Keyword(s):  
Climate Change ◽  
Forecasting Model ◽  
Hydropower Generation ◽  
Grey Forecasting ◽  
Grey Forecasting Model
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