An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting

Hu, Yi‐Chung; Jiang, Peng; Tsai, Jung‐Fa; Yu, Ching-Ying

doi:10.3390/ijerph18020587

Cited by 19 publications

(5 citation statements)

References 37 publications

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“…For performing simulation, carbon emissions dataset of 28 Chinese industries from 1997 to 2015 is used in this study. A Grey model and genetic algorithm based forecasting model is proposed in [17]. In this model, the control parameters of the Grey model are optimized through genetic algorithm.…”

Section: Related Workmentioning

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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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%

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

Mujeeb

Javaid

2023

IET Renewable Power Gen

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“…Digital technologies can also assist energy corporations to achieve carbon neutrality targets in energy regulations. Big data technology is being used to dynamically monitor and account for the carbon emissions of energy companies (Hu et al, 2021) and to build a simulation system for forecasting carbon emissions trends, track energy consumption in the manufacturing process, and accurately measure carbon emissions. Traditional energy enterprises use digital construction in the exploration, mining, and consumption of energy and have also introduced information technology into the manufacturing process, which considerably increases production efficiency and minimizes energy consumption.…”

Section: Literature Review and Development Of Hypothesesmentioning

confidence: 99%

The impact of regional environmental regulations on digital transformation of energy companies: The moderating role of the top management team

Zhang

Jiang

2023

Manage Decis Econ

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How energy businesses undergo digital transformation is a critical topic in light of China's “dual carbon” strategy. Based on institutional theory, upper‐echelon theory, and managerial‐mindset solidification, this article examines how regional environmental regulation affects the digital transformation of energy enterprises from 2016 to 2021 and the moderating role of the top management team in this transformation. The empirical findings show that regional environmental regulations significantly promote the digital transformation of energy companies; however, the average age, education level, and average tenure of the top management team can negatively influence this effect. The results of this study also lead to recommendations on how government agencies and energy enterprises should undertake digital transformation.

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“…is theory has been utilized widely in researches, such as system controlling, forecasting, data clustering, decision-making, and others, and many successful applications in a variety of fields such as economics, agriculture, earthquakes, medicine, industry, and control. erefore, it is proposed as a way to deal with poor, incomplete, or uncertain problems and can be well applied to forecasting and decision-making [10].…”

Section: Research Problemmentioning

confidence: 99%

A Study on Disabling Injuries Prediction of Taiwan Occupational Disaster with Grey Rolling Model

Lin

2022

Mathematical Problems in Engineering

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In order to protect the safety and health of laborers and to achieve the goal of zero occupational accidents at work, the study takes the top three industries with the highest number of laborers inspections from 2010 to 2019, namely, construction, manufacturing, wholesale, and retail as the research object. Using three major indicators of disability injury including Disabling Frequency Rate, Disabling Severity Rate, and Frequency Severity Indicator as parameters, it applies grey theory to establish a GM (1,1) rolling forecast model. It further predicts the trend of disability injuries from 2020 to 2025. Based on the optimized GM (1,1) rolling model, the results show that there has the highest accuracy rate in the prediction of Disabling Frequency Rate (accuracy is 95.235% in K7) in construction. Disabling Severity Rate and Frequency Severity Indicator are both in wholesale and retail industries (accuracy is 97.044% in K6 and accuracy is 99.906% in K5). Therefore, Disabling Severity Rate has an upward trend, which is due to the common type of traffic accidents in the wholesale and retail industry. The study further proposes that relevant actual disaster cases could be the training materials and strengthen the communication in education to improve workers’ safety awareness for occupational disaster prevention.

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An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting

Cited by 19 publications

References 37 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

The impact of regional environmental regulations on digital transformation of energy companies: The moderating role of the top management team

A Study on Disabling Injuries Prediction of Taiwan Occupational Disaster with Grey Rolling Model

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