Hybrid Prediction Model for the Interindustry Carbon Emissions Transfer Network Based on the Grey Model and General Vector Machine

Hu, Ying Cheng; Lv, Kangjuan

doi:10.1109/access.2020.2968585

Cited by 15 publications

(8 citation statements)

References 21 publications

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“…Authors in [16] propose a new hybrid model consisting of the Gery model and General Vector Machine (GVM) for carbon emissions forecasting. In this model, the hyperparameters are optimized by using an artificial fish swarm algorithm.…”

Section: Related Workmentioning

confidence: 99%

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Deep learning based carbon emissions forecasting and renewable energy's impact quantification

Mujeeb

Javaid

2023

IET Renewable Power Gen

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Global warming is one of the most challenging issues of the current era. Revolutions in industrial, Information and Communication Technology (ICT) sectors significantly contribute in increasing global warming. Green House Gases (GHG) emissions from industrial, transportation, power and other sectors cause environmental pollution, which results in climate degradation. Environmental experts are well aware of the disastrous consequences of excessive global warming; therefore, several decarbonization strategies are developed and practiced in the recent past. A widely practiced decarbonization strategy is replacing fossil fuels by Renewable Energy Sources (RES) in the power systems. Electricity consumers are also encouraged to shift their consumption load to low carbon emissions' time periods. For accomplishing this task, an estimation of future carbon emissions is required. In this paper, power system's carbon emissions are predicted accurately with the help of a novel and an efficient forecasting model. The proposed model comprises of Spearman Correlation Analysis (SCA) and Improved Shallow Denoising Autoencoder (ISDAE) based feature engineering. Forecasting is performed through an Improved Particle Swarm Optimization (IPSO) based Deep Neural Network (DNN) forecaster. In addition, a comprehensive quantification analysis is also presented in this paper. The impacts of RES integration level on the electricity price, consumption cost and Greenhouse Gases (GHG) emissions are quantified descriptively and graphically. Performance of the proposed forecasting model is evaluated by Normalized Root Mean Square Error (NRMSE), Mean Absolute Error (MAE) and Mean Square Error (MSE). Simulation results prove that the proposed model outperforms Support Vector Machine (SVM) and Multiple Linear Regression (MLR) based carbon emission forecasting models in terms of forecasting accuracy.

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Section: Related Workmentioning

confidence: 99%

“…Table 2 shows the fossil fuel based carbon emissions of several countries and their percentages in global emissions in 2019 [7]. Many researchers conduct studies for analyzing the carbon emissions behavior and its forecasting [8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Deep learning based carbon emissions forecasting and renewable energy's impact quantification

Mujeeb

Javaid

2023

IET Renewable Power Gen

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“…Scholars have used dynamic scenario simulation models to analyze the future trend of carbon emissions, for example, by combining such a model with the Monte Carlo simulation method to construct a new dynamic scenario simulation model (Hu and Lv 2020 ), discussing the dynamic evolution trajectory of China’s building carbon emissions, and then combining this model with specific data to provide effective information for the government (Huo et al 2021a , b ).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, scholars have combined the two models to establish a kind of combined model suitable for small sample data with optimized parameters and performance. Hu and Lv ( 2020 ) combined the grey model with the optimized vector machine algorithm to simulate the carbon emission transfer network among various industries. Huang et al ( 2019 ) used grey correlation analysis to identify the strongly correlated factors of carbon emissions and then used the long short-term memory (LSTM) method to predict China’s carbon emissions.…”

Section: Introductionmentioning

confidence: 99%

A novel grey prediction model with a feedforward neural network based on a carbon emission dynamic evolution system and its application

Nie

Duan

2022

Environ Sci Pollut Res

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The objective and accurate prediction of carbon dioxide emissions holds great significance for improving governmental energy policies and plans. Therefore, starting from an evolutionary system of carbon emissions, this paper studies the evolution of the system, establishes a grey model of the system, and expands the modeling structure of this model. The modeling mechanism of the classical feedforward neural network model is organically combined with the function of the external influencing factors of carbon emissions, and the grey model of the carbon emission dynamic system is established with a neural network. Then, the properties of the model are studied, the parameters of the model are optimized, and the modeling steps are obtained. Finally, the validity of the model is analyzed by using the carbon emissions of Beijing from 2009 to 2018. The results of the four cases show that the simulation and prediction errors of the new model are all less than 10%, and case 1 shows the best results of 1.56% and 2.07%, respectively, which are used to predict the carbon dioxide emissions in the next 5 years in Beijing. The prediction results are in accordance with the actual trend, which indicates the effectiveness and feasibility of the model.

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“…Application of the combination of the grey prediction model and other models to carbon emissions: Hu et al (2020) combined the grey model with the optimized vector machine algorithm to simulate the carbon emission transfer network among various industries and effectively predicted the carbon emission transfer value. Huang et al (2019) used grey correlation analysis to identify the strongly correlated factors of carbon emissions and then used the long, short-term memory (LSTM) method to predict China's carbon emissions.…”

mentioning

confidence: 99%

A Novel Grey Model with a Neural Network in Carbon Emission Dynamic System

Nie

Duan

2022

Preprint

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Carbon dioxide emissions have received widespread attention and have become one of the most important research topics in the world. The objective and accurate prediction of carbon dioxide emissions holds great significance for improving government energy policies and plans. Therefore, starting from an evolutionary system of carbon emissions, this paper studies the evolution of the system, establishes a grey model of the evolutionary dynamic system of carbon emissions, and expands the modelling structure of the grey model. The modelling mechanism of the neural network model is organically combined with the function of the external influencing factors of carbon emissions, and the carbon emission dynamic system's grey model with a neural network is established, which expands the modelling object of the neural network method. Then, the properties of the model are studied, the parameters of the model are optimized, and the modelling steps of the model are obtained. Finally, the validity of the new model is analysed by using the carbon emissions of Beijing from 2009 to 2018. Four different modelling objects show that the new model has good simulation and prediction accuracy. Furthermore, we choose the best one to predict carbon dioxide emissions over the next five years. The results show that the existing measures taken by Beijing for carbon dioxide emissions are effective and have controlled the increase in carbon emissions to a certain extent. However, it is necessary to continue to strictly control carbon emissions and further improve the measures to achieve the goal of a continuous reduction in carbon emissions.

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Hybrid Prediction Model for the Interindustry Carbon Emissions Transfer Network Based on the Grey Model and General Vector Machine

Cited by 15 publications

References 21 publications

Deep learning based carbon emissions forecasting and renewable energy's impact quantification

Deep learning based carbon emissions forecasting and renewable energy's impact quantification

A novel grey prediction model with a feedforward neural network based on a carbon emission dynamic evolution system and its application

A Novel Grey Model with a Neural Network in Carbon Emission Dynamic System

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