Day ahead carbon emission forecasting of the regional National Electricity Market using machine learning methods

Aryai, Vahid; Goldsworthy, Mark

doi:10.1016/j.engappai.2023.106314

Cited by 10 publications

(2 citation statements)

References 39 publications

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“…The authors suggested that causal inference methods could be introduced to validate the causal relationship between influencing factors and carbon emissions in order to identify more significant influencing factors. Aryai et al [11] proposed a PSO-ERT regression model for predicting the emission intensity in Australia's regional electricity market. Sarwar et al [12] selected a suitable model for predicting electricity prices and carbon emissions in the eastern region of Saudi Arabia.…”

Section: Related Workmentioning

confidence: 99%

Coupling LSTM and CNN Neural Networks for Accurate Carbon Emission Prediction in 30 Chinese Provinces

Han,

Cui,

et al. 2023

Sustainability

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Global warming is a major environmental issue facing humanity, and the resulting climate change has severely affected the environment and daily lives of people. China attaches great importance to and actively responds to climate change issues. In order to achieve the “dual carbon” goal, it is necessary to clearly define the emission reduction path and scientifically predict future carbon emissions, which is the basis for setting emission reduction targets. To ensure the accuracy of data, this study applies the emission coefficient method to calculate the carbon emissions from the energy consumption in 30 provinces, regions, and cities in China from 1997 to 2021. Considering the spatial correlation between different regions in China, we propose a new machine learning prediction model that incorporates spatial weighting, namely, an LSTM-CNN combination model with spatial weighting. The spatial weighting explains the spatial correlation and the combined model is used to analyze the carbon emissions in the 30 provinces, regions, and cities of China from 2022 to 2035 under different scenarios. The results show that the LSTM-CNN combination model with four convolutional layers performs the best. Compared with other models, this model has the best predictive performance, with an MAE of 8.0169, an RMSE of 11.1505, and an R2 of 0.9661 on the test set. Based on different scenario predictions, it is found that most cities can achieve carbon peaking before 2030. Some cities need to adjust their development rates based on their specific circumstances in order to achieve carbon peaking as early as possible. This study provides a research direction for deep learning time series forecasting and proposes a new predictive method for carbon emission forecasting.

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Section: Related Workmentioning

confidence: 99%

Coupling LSTM and CNN Neural Networks for Accurate Carbon Emission Prediction in 30 Chinese Provinces

Han,

Cui,

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Awareness of the importance of information on future emission levels is one of the crucial steps in planning to reduce GHG emissions [10]. Based on this, emission prediction can be used to implement emission reduction strategies effectively [11]. At the United Nations COP26 Conference on Climate Change in 2021, opportunities to reduce GHG emissions were examined in 28 different axes as part of efforts to prevent global warming.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Predictive Model as an Emission Reduction Strategy Based on Power Plants’ Fuel Consumption Activity

Herawati,

Gary,

Hikmawati

et al. 2024

IEEE Access

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The Indonesian government announced that the first phase of carbon trading will be implemented in 2023, starting with coal-fired power plants (PLTU). One of the PLTUs in Indonesia, a case study in this research, still identifies greenhouse gas (GHG) emissions manually and has not provided information supporting the efficiency of future carbon trading activities. Based on this, the research focuses on developing a predictive model that can provide information on carbon emission predictions, coal fuel consumption, and gross electricity as a strategy for carbon emission reduction efforts using The Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. The development of the predictive model was built by combining time series models to predict each variable as an input to a regression model to predict carbon emissions. The evaluation used the MAE, RMSE, and MAPE metrics. Model training used five machine learning models: Linear Regression, Support Vector Regression, Decision Tree Regression, Random Forest Regression, and LightGBM. The time series model testing results for PLTU Units 3 and 7 produced the best model, Linear Regression, while PLTU Unit 8 had the best model, Random Forest Regression. Subsequently, the regression model testing results for all units produced the best model, Linear Regression. With this study, PLTU can know the prediction of carbon emissions and plan carbon emission reduction strategies by estimating gross electricity outcomes and considering the predicted results of coal fuel consumption and carbon emission predictions, thus allowing PLTU to assess the impact from both environmental and financial perspectives.INDEX TERMS coal-fired power plants, emissions prediction, greenhouse gases emissions, machine learning.

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CO2 emission prediction based on carbon verification data of 17 thermal power enterprises in Gansu Province

Shi,

Yang,

Qiao

et al. 2023

Environ Sci Pollut Res

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Day ahead carbon emission forecasting of the regional National Electricity Market using machine learning methods

Cited by 10 publications

References 39 publications

Coupling LSTM and CNN Neural Networks for Accurate Carbon Emission Prediction in 30 Chinese Provinces

Coupling LSTM and CNN Neural Networks for Accurate Carbon Emission Prediction in 30 Chinese Provinces

A Hybrid Predictive Model as an Emission Reduction Strategy Based on Power Plants’ Fuel Consumption Activity

CO2 emission prediction based on carbon verification data of 17 thermal power enterprises in Gansu Province

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