scholarly journals An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting

2021 ◽  
Vol 18 (2) ◽  
pp. 587
Author(s):  
Yi-Chung Hu ◽  
Peng Jiang ◽  
Jung-Fa Tsai ◽  
Ching-Ying Yu
Keyword(s):  
Carbon Dioxide ◽  
Prediction Model ◽  
Carbon Dioxide Emissions ◽  
Prediction Models ◽  
International Energy Agency ◽  
Carbon Dioxide Emission ◽  
Control Variable ◽  
Grey Prediction ◽  
Emission Data ◽  
Grey Prediction Model

Because grey prediction does not demand that the collected data have to be in line with any statistical distribution, it is pertinent to set up grey prediction models for real-world problems. GM(1,1) has been a widely used grey prediction model, but relevant parameters, including the control variable and developing coefficient, rely on background values that are not easily determined. Furthermore, one-order accumulation is usually incorporated into grey prediction models, which assigns equal weights to each sample, to recognize regularities embedded in data sequences. Therefore, to optimize grey prediction models, this study employed a genetic algorithm to determine the relevant parameters and assigned appropriate weights to the sample data using fractional-order accumulation. Experimental results on the carbon dioxide emission data reported by the International Energy Agency demonstrated that the proposed grey prediction model was significantly superior to the other considered prediction models.

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.

scholarly journals The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tongfei Lao ◽  
Xiaoting Chen ◽  
Jianian Zhu
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Small Sample ◽  
Grey Prediction ◽  
Dynamic Background ◽  
Background Values ◽  
Grey Prediction Model ◽  
Model Based

As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM (1, N) model is the most popular and important grey model, in which the first “1” stands for the “first order” and the second “N” represents the “multivariate.” The construction of the background values is not only an important step in grey modeling but also the key factor that affects the prediction accuracy of the grey prediction models. In order to further improve the prediction accuracy of the multivariate grey prediction models, this paper establishes a novel multivariate grey prediction model based on dynamic background values (abbreviated as DBGM (1, N) model) and uses the whale optimization algorithm to solve the optimal parameters of the model. The DBGM (1, N) model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy. It is a grey prediction model with extremely strong adaptability. Finally, four cases are used to verify the feasibility and effectiveness of the model. The results show that the proposed model significantly outperforms the other 2 multivariate grey prediction models.

scholarly journals Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaoshuang Luo ◽  
Bo Zeng ◽  
Hui Li ◽  
Wenhao Zhou
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Prediction Models ◽  
Grey Model ◽  
The Novel ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Combination Optimization ◽  
Installed Capacity ◽  
Novel Model

The intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable solution to meet this challenge. However, the single parameter optimization of the conventional grey model has some limitations in improving its modeling ability. To this end, a novel grey prediction model with parameters combination optimization is proposed in this paper. Firstly, considering the modeling mechanism and process, the order of accumulation generation of the grey prediction model is optimized by Particle Swarm Optimization (PSO) Algorithm. Secondly, as different orders of accumulation generation correspond to different parameter matrixes, the background value coefficient of the grey prediction model is optimized based on the optimal accumulation order. Finally, the novel model of combinational optimization is employed to simulate and forecast Chinese WPIC, and the comprehensive error of the novel model is only 1.34%, which is superior to the other three grey prediction models (2.82%, 1.68%, and 2.60%, respectively). The forecast shows that China’s WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the standpoint of the practitioners and governments.

Non-equidistant non-homogenous grey prediction model with fractional accumulation and its application

2021 ◽  
pp. 1-14
Author(s):  
Jia-Nian Zhu ◽  
Xu-Chong Liu ◽  
Chong Liu
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Model Based ◽  
Whale Optimization ◽  
High Prediction ◽  
Best Parameters ◽  
Better Than

Non-equidistant non-homogenous grey model (abbreviated as NENGM (1,1, k) model) is a grey prediction model suitable for predicting time series with non-equal intervals. It is widely used in various fields of society due to its high prediction accuracy and strong adaptability. In order to further improve the prediction accuracy of the NENGM (1,1, k) model, the NENGM (1,1, k) model is optimized in terms of the cumulative order and background value of the NENGM (1,1, k) model, and a NENGM (1,1, k) model based on double optimization is established (abbreviated as FBNENGM (1,1, k) model), and the whale optimization algorithm is used to solve the best parameters of the model. In order to verify the feasibility and validity of the FBNENGM (1,1, k) model, the FBNENGM (1,1, k) model and other four prediction models are applied to three cases respectively, and three indexes commonly used to evaluate the performance of prediction models are used to distinguish. The results show that the prediction accuracy of the FBNENGM (1,1, k) model based on double optimization is better than other prediction models.

scholarly journals Research on a Grey Prediction Model of Population Growth Based on a Logistic Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Mingyu Tong ◽  
Zou Yan ◽  
Liu Chao
Keyword(s):  
Population Growth ◽  
Prediction Model ◽  
Growth Model ◽  
Prediction Models ◽  
Least Squares Method ◽  
Grey Prediction ◽  
Growth Prediction ◽  
New Model ◽  
Grey Prediction Model ◽  
Advantages And Disadvantages

The classical population growth models include the Malthus population growth model and the logistic population growth model, each of which has its advantages and disadvantages. To address the disadvantages of the two models, this paper establishes a grey logistic population growth prediction model, based on the modeling mechanism of the grey prediction model and the characteristics of the logistic model, which uses the least-squares method to estimate the maximum population capacity. In accordance with the data characteristics of population growth, the weakening buffer operator is used to establish the weakening buffer operator grey logistic population growth prediction model, which improves its accuracy, thus improving the classic population prediction model. Four actual case datasets are used simultaneously, and the two classical grey prediction models are compared. The results of the six evaluation indicators show that the effects of the new model demonstrate obvious advantages. Finally, the new model is applied to the population forecast of Chongqing, China. The prediction results suggest that the population may reach a peak in 2020 and decline in the future. This finding is consistent with the logistic population growth model.

scholarly journals The Prediction of Carbon Dioxide Emission Using ARIMA for Support Green Energy Development in Surabaya Municipality

KnE Energy ◽  
2015 ◽  
Vol 2 (2) ◽  
pp. 106
Author(s):  
Juniarko Prananda ◽  
Ridho Hantoro ◽  
Gunawan Nugroho
Keyword(s):  
Carbon Dioxide ◽  
Air Quality ◽  
Prediction Model ◽  
Carbon Dioxide Emissions ◽  
Negative Impact ◽  
Carbon Dioxide Emission ◽  
Industrial Sector ◽  
Green Energy ◽  
Emission Growth ◽  
The Impact

<p>Surabaya is a metropolitan city in Indonesia. As well as the second largest city in Indonesia, with average yearly GDP growth of Surabaya increased 5 % of the national GDP, Surabaya has attraction for the urbanization and very strategic for the investors to build their industry in Surabaya. This leads to industrial sector, transportation, and the number of civilians growing rapidly which has the impact on the air quality in Surabaya. The most influential of air quality is the emission of Carbon Dioxide (CO2). Carbon Dioxide is a greenhouse gases that provide the largest contribution to global warming and climate change. This has negative impact to humans, so it is necessary conduct a research on the Carbon Dioxide emission growth being produced from the transportation, the industrial Sector, and household in Surabaya. The purpose of this research is to get prediction model for Carbon Dioxide emission growth being produced in the Surabaya. The prediction model can be represented the carbon Dioxide emissions growth in the next years that would give recommendations to the Surabaya Municipality Government for preventive action to reduce the Carbon Dioxide emission in Surabaya. </p><p><strong>Keywords</strong>: Carbon dioxide; emission; prediction <br /><br /></p>

scholarly journals The Problem of Carbon Dioxide Emissions from Closed Coal Mine Shafts – The Overview and the Case Study

2016 ◽  
Vol 61 (3) ◽  
pp. 587-600
Author(s):  
Paweł Wrona ◽  
Józef Sułkowski ◽  
Zenon Różański ◽  
Grzegorz Pach
Keyword(s):  
Carbon Dioxide ◽  
Carbon Dioxide Emissions ◽  
Carbon Dioxide Emission ◽  
Mining Area ◽  
Emission Point ◽  
Water Pumping ◽  
Gas Hazard ◽  
Flow Through ◽  
Reporting Procedures

Abstract Greenhouse gas emissions are a common problem noticed in every mining area just after mine closures. However, there could be a significant local gas hazard for people with continuous (but variable) emission of these gases into the atmosphere. In the Upper Silesia area, there are 24 shafts left for water pumping purposes and gases can flow through them hydraulically. One of them – Gliwice II shaft – was selected for inspection. Carbon dioxide emission with no methane was detected here. Changes in emission and concentration of carbon dioxide around the shaft was the aim of research carried out. It was stated that a selected shaft can create two kinds of gas problems. The first relates to CO2 emission into the atmosphere. Possible emission of that gas during one minute was estimated at 5,11 kg CO2/min. The second problem refers to the local hazard at the surface. The emission was detected within a radius of 8m from the emission point at the level 1m above the ground. These kinds of matters should be subject to regular gas monitoring and reporting procedures.

Dynamic Analysis and Prediction of Ecological Footprint in Jilin Province of China Based on Grey Prediction Model

2014 ◽  
Vol 472 ◽  
pp. 899-903 ◽  
Author(s):  
Biao Gao ◽  
Qing Tao Xu
Keyword(s):  
Prediction Model ◽  
Ecological Footprint ◽  
Jilin Province ◽  
Grey Prediction ◽  
Dynamic Changes ◽  
Ecological Capacity ◽  
Grey Prediction Model ◽  
Ecological Deficit ◽  
Gray Prediction ◽  
Per Capita

The paper calculates ecological footprint per capita and ecological capacity per capita in the Jilin province during 1998 and 2010 by using the ecological footprint theory, and analyzes the dynamic changes of ecological footprint per capita and ecological capacity per capita, and obtains development prediction model of ecological footprint per capita and ecological capacity per capita based on grey prediction model. The results indicate the ecological footprint per capita had increased continuously from 1.7841 hm2 per capita to 3.2013 hm2 per capita between 1998 and 2010. During this period, ecological capacity per capita dropped from 1.3535 hm2 per capita to 1.3028 hm2 per capita. Ecological deficit had increased from 0.4306 hm2 per capita to 1.8985 hm2 per capita that showed that the development of Jilin province was in an unsustainable status. The gray prediction model shows the ecological footprint per capita in the Jilin province will increase from 3.4833 hm2 per capita to 5.7022 hm2 per capita between 2011 and 2020, ecological capacity per capita will drop from 1.2978 hm2 per capita to 1.2676 hm2 per capita and ecological deficit will increase from 2.1855 hm2 per capita to 4.4346 hm2 per capita.

Driving Factors Analysis of Carbon Dioxide Emissions in China Based on STIRPAT Model

2013 ◽  
Vol 734-737 ◽  
pp. 1910-1914 ◽  
Author(s):  
Qiao Zhi Zhao ◽  
Qing You Yan
Keyword(s):  
Economic Growth ◽  
Carbon Dioxide ◽  
Carbon Dioxide Emissions ◽  
High Speed ◽  
Carbon Dioxide Emission ◽  
Urbanization Process ◽  
Stirpat Model ◽  
Factors Analysis ◽  
Secondary Industry

China is developing at relatively high speed, not only the regional development speed should be focused upon, but also the environmental impact of economic growth should be paid attention to, especially the level change of carbon dioxide emission. To some degree, quantity of carbon dioxide emission has become one of the most important indexes for measuring quality of a nations economic growth. Thus, this thesis is trying to analyze the driving relations between economic growth and carbon dioxide. Upon STIRPAT model, ridge regression method and elasticity theory are applied to analyze the influencing factors of carbon dioxide quantity such as the population quantity, Chinas urbanization process, per capita GDP, energy density and the percentage of the secondary industry. Correspondingly, based on the different influencing variables to carbon dioxide emission quantity, needy measures are brought out to control and decrease emissions. Feasible suggestions are trying to improve Chinas economic development quality.

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