Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series

Thanh Tuan Le; Prabhakar Sharma; Sameh M. Osman; Marek Dzida; Phuoc Quy Phong Nguyen; Minh Ho Tran; Dao Nam Cao; Viet Dung Tran

doi:10.1007/s10098-024-02852-9

Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series

Abstrakt

This study assessed the usefulness of algorithms in estimating energy consumption and carbon dioxide emissions in Viet- nam, in which the training dataset was used to train the models linear regression, random forest, XGBoost, and AdaBoost, allowing them to comprehend the patterns and relationships between population, GDP, and carbon dioxide emissions, energy consumption. The results revealed that random forest, XGBoost, and AdaBoost outperformed linear regression. Furthermore, for random forest, XGBoost, and AdaBoost, the coefficients of determination were higher, indicating a better fit to the data. Moreover, time series forecasting models such as autoregressive integrated moving average, seasonal autore- gressive integrated moving average, and exponential smoothing were used to predict future energy consumption and carbon dioxide emissions in Vietnam. The models were trained and verified using historical data. The time series model findings showed that energy consumption rose steadily during the predicted timeframe. The autoregressive integrated moving aver- age model predicted 162258.77 ktoe of energy use by 2050, whereas the seasonal autoregressive integrated moving average and exponential smoothing modes predicted 160673.8 ktoe and 153206.44 ktoe of energy use, respectively. By 2050, the autoregressive integrated moving average model anticipated 6.51 metric tons of carbon dioxide emissions per capita, the SARIMA model 7.769 metric tons, and the exponential smoothing model 6.22 metric tons. The findings show how machine learning techniques and time series models may be used to estimate energy usage and carbon dioxide emissions in Vietnam. These insights could assist Vietnam government in making informed judgments concerning energy planning and policy development

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Autorzy (8)

Thanh Tuan Le
Prabhakar Sharma
Sameh M. Osman
Marek Dzida dr hab. inż.
Phuoc Quy Phong Nguyen
Minh Ho Tran
Dao Nam Cao
Viet Dung Tran

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Słowa kluczowe

ENERGY CONSUMPTION · CO2 EMISSION · TIME SERIES FORECASTING · PREDICTIVE ALGORITHMS · ENERGY PLAN · EMISSION POLICY

Informacje szczegółowe

Kategoria:

Publikacja w czasopiśmie

Typ:

artykuły w czasopismach dostępnych w wersji elektronicznej [także online]

Opublikowano w:

Clean Technologies and Environmental Policy
ISSN: 1618-954X

Język:

angielski

Rok wydania:

2024

Opis bibliograficzny:

Le T. T., Sharma P., Osman S. M., Dzida M., Nguyen P. Q. P., Tran M. H., Cao D. N., Tran V. D., Forecasting energy consumption and carbon dioxide emission of Vietnam by prognostic models based on explainable machine learning and time series, Clean Technologies and Environmental Policy, 2024,10.1007/s10098-024-02852-9

DOI: