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Machine Learning to Predict Energy Usage Outcomes

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  • James Zhang Sir Winston Churchill Highscool

DOI:

https://doi.org/10.58445/rars.1659

Keywords:

Sustainable Development, Machine Learning, Regressions

Abstract

How realistic are the sustainability standards that we impose on countries? Using data from countries all around the world from 2000-2020, this paper answers this question. Using a regression model, we predicted the renewable energy share in the total final energy consumption and CO2 emissions using various different factors that impact a country. The model has an r-squared value of 0.94 for renewable energy share in the total final energy consumption and 0.99 for CO2 emissions. The developed model has the potential to be used as a tool to achieve realistic progress towards sustainable development. 

References

United Nations. (n.d.). Global issues. United Nations. https://www.un.org/en/global-issues

Pollution by Country 2024. Pollution by country 2024. (2024a). https://worldpopulationreview.com/country-rankings/pollution-by-country

Tanwar, A. (2023, August 19). Global Data on Sustainable Energy (2000-2020). Kaggle. https://www.kaggle.com/datasets/anshtanwar/global-data-on-sustainable-energy

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2024-09-21

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Copyright (c) 2024 James Zhang

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.