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CO2 emission prediction from coal used in power plants: a machine learning-based approach

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Abstract

The utilization of fossil fuels has led to a significant rise in carbon dioxide (CO2) emission. Among various sectors, the energy industry plays a substantial role in contributing to global CO2 emission. In this research, we have employed both multivariate and univariate time-series models, as well as machine learning and deep learning techniques, to analyze a dataset on CO2 emission. The dataset, obtained from the central electricity authority, encompasses attributes such as coal supply information, CO2 emission, peak demand, and peak met. To evaluate the performance of the applied models, we have utilized several performance metrics including RMSPE, MAE, RMSE, MSE, MAPE, SMAPE, and RAE. The dataset spans from 2005 to 2021, enabling us to conduct training and testing. Furthermore, we have utilized the best-performing models to forecast CO2 emission from 2022 to 2050. The results of our study demonstrate that autoregression is the top-performing model, achieving a remarkable ranking of 1.85 according to the Friedman ranking. Additionally, we have conducted a comparative analysis between multivariate and univariate approaches.

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Data availability

The data supporting this study's findings are openly available and will also be provided on request.

Notes

  1. https://www.iea.org/energy-system/energy-efficiency-and-demand.

  2. https://www.iea.org/data-and-statistics/data-product/world-energy-balances.

  3. https://www.iea.org/reports/world-energy-outlook-2023.

  4. https://cea.nic.in/fuel-reports/?lang=en

Abbreviations

CEA:

Central Electricity Authority

LSTM:

Long short-term memory

AIC:

Akaike information criterion

GHG:

Greenhouse gases

GDP:

Gross domestic product

ISA:

International Solar Alliance

ARIMA:

Autoregressive integrated moving average

VARIMA:

Vector autoregressive integrated moving average

References

  1. Oberschelp, C., Pfister, S., Raptis, C. , Hellweg, S.: ETH Library Global emission hotspots of coal power generation. (2019). https://doi.org/10.3929/ethz-b-000324460

  2. Wu, X., Fu, B., Wang, S., Song, S., Li, Y., Xu, Z., Wei, Y., Liu, J.: Decoupling of SDGs followed by re-coupling as sustainable development progresses. Nat. Sustain. 5(5), 452–459 (2022). https://doi.org/10.1038/s41893-022-00868-x

    Article  Google Scholar 

  3. Yan, W., Yang, J., Zhao, Z., Yang, J., Yang, W.: Global matrix method for frequency-domain stability analysis of hydropower generating system. J. Clean. Prod. 333, 10 (2022). https://doi.org/10.1016/j.jclepro.2021.130097

    Article  Google Scholar 

  4. Awe, Y.: What You Need to Know About Climate Change and Air Pollution. World Bank (2022). https://www.worldbank.org/en/news/feature/2022/09/01/what-you-need-to-know-about-climatechange-and-air-pollution

  5. Bakre, A., Sengupta, A., Wadhwa, D., Kumar, M.: Impact of Energy Efficiency Measures For The Year 2021-22. Bureau of Energy Efficiency, Gurgoan (2023). https://beeindia.gov.in/sites/default/files/publications/files/Impact%20Assessment%202021-22_%20FINAL%20Report_June%202023.pdf

  6. Kopas, J., York, E., Jin, X., Harish, S.P., Kennedy, R., Shen, S.V., Urpelainen, J.: Environmental justice in India: incidence of air pollution from coal-fired power plants. Ecol. Econ. 176, 106711 (2020). https://doi.org/10.1016/j.ecolecon.2020.106711

    Article  Google Scholar 

  7. Kumar, S., Managi, S., Jain, R.K.: CO2 mitigation policy for Indian thermal power sector: Potential gains from emission trading. Energy Economics 86, 104653 (2020). https://doi.org/10.1016/j.eneco.2019.104653

    Article  Google Scholar 

  8. Kumar, S., Mishra, S., Singh, S.K.: A machine learning-based model to estimate PM2.5 concentration levels in Delhi’s atmosphere. Heliyon 6(11), e05618 (2020). https://doi.org/10.1016/j.heliyon.2020.e05618

    Article  Google Scholar 

  9. Sahu, S.K., Zhu, S., Guo, H., Chen, K., Liu, S., Xing, J., Zhang, H.: Contributions of power generation to air pollution and associated health risks in India: current status and control scenarios. J. Clean. Prod. 288, 125587 (2021)

    Article  Google Scholar 

  10. Zelinka, D., & Mitova, S. (n.d.). Reducing CO 2 Emissions by Targeting the World’s Hyper-Polluting Power Plants.

  11. Diluiso, F., Walk, P., Manych, N., Cerutti, N., Chipiga, V., Workman, A., Ayas, C., Cui, R.Y., Cui, D., Song, K., Banisch, L.A., Moretti, N., Callaghan, M.W., Clarke, L., Creutzig, F., Hilaire, J., Jotzo, F., Kalkuhl, M., Lamb, W.F., Minx, J.C.: Coal transitions—Part 1: a systematic map and review of case study learnings from regional, national, and local coal phase-out experiences. Environ. Res. Lett. (2021). https://doi.org/10.1088/1748-9326/ac1b58

    Article  Google Scholar 

  12. Hower, J.C., Groppo, J.G.: Rare Earth-bearing particles in fly ash carbons: Examples from the combustion of eastern Kentucky coals. Energy Geosci. 2(2), 90–98 (2021). https://doi.org/10.1016/j.engeos.2020.09.003

    Article  Google Scholar 

  13. Gasparotto, J., Da Boit Martinello, K.: Coal as an energy source and its impacts on human health. Energy Geosci. 2(2), 113–120 (2021). https://doi.org/10.1016/j.engeos.2020.07.003

    Article  Google Scholar 

  14. Ağbulut, Ü.: Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms. Sustain. Prod. Consum. 29, 141–157 (2022). https://doi.org/10.1016/j.spc.2021.10.001

    Article  Google Scholar 

  15. Nepal, R., Paija, N.: A multivariate time series analysis of energy consumption, real output and pollutant emissions in a developing economy: New evidence from Nepal. Econ. Model. 77(May), 164–173 (2019). https://doi.org/10.1016/j.econmod.2018.05.023

    Article  Google Scholar 

  16. Qader, M.R., Khan, S., Kamal, M., Usman, M., Haseeb, M.: Forecasting carbon emissions due to electricity power generation in Bahrain. Environ. Sci. Pollut. Res. 29(12), 17346–17357 (2022). https://doi.org/10.1007/s11356-021-16960-2

    Article  Google Scholar 

  17. Bakay, M.S., Ağbulut, Ü.: Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms. J. Clean. Prod. 285, 125324 (2021). https://doi.org/10.1016/j.jclepro.2020.125324

    Article  Google Scholar 

  18. Ofosu-Adarkwa, J., Xie, N., Javed, S.A.: Forecasting CO2 emissions of China’s cement industry using a hybrid Verhulst-GM(1, N) model and emissions’ technical conversion. Renew. Sustain. Energy Rev. (2020). https://doi.org/10.1016/j.rser.2020.109945

    Article  Google Scholar 

  19. Xu, N., Ding, S., Gong, Y., Bai, J.: Forecasting Chinese greenhouse gas emissions from energy consumption using a novel grey rolling model. Energy 175(2019), 218–227 (2019). https://doi.org/10.1016/j.energy.2019.03.056

    Article  Google Scholar 

  20. De Stefani, J., Le Borgne, Y.A., Caelen, O., Hattab, D., Bontempi, G.: Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting. Int. J. Data Sci. Anal. 7(4), 311–329 (2019). https://doi.org/10.1007/s41060-018-0150-x

    Article  Google Scholar 

  21. Liu, F., Cai, M., Wang, L., Lu, Y.: An Ensemble Model Based on Adaptive Noise Reducer and Over-Fitting Prevention LSTM for Multivariate Time Series Forecasting. IEEE Access 7, 26102–26115 (2019). https://doi.org/10.1109/ACCESS.2019.2900371

    Article  Google Scholar 

  22. Kumari, S., Singh, S.K.: Machine learning-based time series models for effective CO2 emission prediction in India. Environ. Sci. Pollut. Res. 0123456789, 1932–1937 (2022). https://doi.org/10.1109/icaccs54159.2022.9785100

    Article  Google Scholar 

  23. Ameyaw, B., Yao, L.: Analyzing the impact of GDP on CO2 emissions and forecasting Africa’s total CO2 emissions with non-assumption driven bidirectional long short-term memory. Sustainability (Switzerland) (2018). https://doi.org/10.3390/su10093110

    Article  Google Scholar 

  24. Noor, N.M., Al Bakri Abdullah, M.M., Yahaya, A.S., Ramli, N.A.: Comparison of linear interpolation method and mean method to replace the missing values in environmental data set. Mater. Sci. Forum 803, 278–281 (2015). https://doi.org/10.4028/www.scientific.net/MSF.803.278

    Article  Google Scholar 

  25. Wei, W.W.S.: Oxford Handbooks Online Time Series Analysis (Vol. 2, Issue April 2018), (2018). https://doi.org/10.1093/oxfordhb/9780199934898.013.0022

  26. Bengio, Y., Razvan Pascanu, T.M.: On the difficulty of training recurrent neural networks. Phylogenet. Diversi. Appl. Challenges Biodivers. Sci. 2, 41–71 (2018). https://doi.org/10.1007/978-3-319-93145-6_3

    Article  Google Scholar 

  27. Jurgen Schmidhuber, S.H.: Long Short-Term Memory. Routledge Libr Ed Linguist Mini-Set A Gener Linguist 2–11(8), 13–35 (2013). https://doi.org/10.3138/9781487583064-002

    Article  Google Scholar 

  28. Gulcehre, C., Cho, K., Pascanu, R., Bengio, Y.: Learned-norm pooling for deep feedforward and recurrent neural networks. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2014, Nancy, France, 15–19 September 2014. Proceedings, Part I, pp. 530–546. Springer, Berlin, Heidelberg (2014)

  29. Shcherbakov, M.V., Brebels, A., Shcherbakova, N.L., Tyukov, A.P., Janovsky, T.A., Kamaev, V.A., evich: A survey of forecast error measures. World Appl. Sci. J. 24(24), 171–176 (2013). https://doi.org/10.5829/idosi.wasj.2013.24.itmies.80032

    Article  Google Scholar 

  30. Kim, K.G.: Deep learning book review. Nature 29(7553), 1–73 (2019)

    Google Scholar 

  31. Kukačka, J., Golkov, V., Cremers, D. Regularization for deep learning: a taxonomy. (2017).

  32. García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf. Sci. 180(10), 2044–2064 (2010). https://doi.org/10.1016/j.ins.2009.12.010

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Information Technology, Mahatma Gandhi Central University, Motihari, Bihar, India

    Ankit Prakash & Sunil Kumar Singh

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  1. Ankit Prakash
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  2. Sunil Kumar Singh
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Contributions

AP: introduction, literature review, and performed the experiments, and wrote the first draft of the manuscript. SKS: conceptualized and designed the experiments, analyzed and interpreted the results.

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Correspondence to Sunil Kumar Singh.

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Prakash, A., Singh, S.K. CO2 emission prediction from coal used in power plants: a machine learning-based approach. Iran J Comput Sci (2024). https://doi.org/10.1007/s42044-024-00185-w

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