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Wind Power Forecasting Model Through Data Analysis of Causes and Impacts of Global Warming

Received: 23 September 2021     Accepted: 8 October 2021     Published: 19 October 2021
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Abstract

In this article, we aim to spread the need for change by informing the reader about global warming and its consequences. Global warming is a phenomenon in which average global temperatures rise due to the increase in greenhouse gases (GHG) emitted and trapped in the atmosphere. These specific gases are special in that they contribute to the flow of heat within the atmosphere as they trap more heat from the sun within the atmosphere. The most influential of these gases is CO2, which most of the data analysis in this article will be focusing on. There are many issues and problems that are attributed to global warming, along with a wide array of data that relates to the topic. By analyzing such data, one can find out the global trend in climate change and even come up with potential solutions for this increasingly threatening event. The lack of action taken by world leaders is due to general negligence and a lack of an effective and coherent solution to tackle climate change. A probable solution would be to find a surrogate to the main cause of GHG emissions, which is the substantial exploitation of conventional (nonrenewable) energy sources. Renewable energy sources are excellent substitutes to conventional energy sources and one of the most effective types is wind. This energy source is currently implemented in many areas around the globe and is producing better results than in the past when conventional energy sources were in use. Although wind turbines are more efficient than solar panels in producing electricity because they are not affected by the presence of sunlight, the amount of electricity generated can fluctuate due to various factors. To adjust to these varying conditions, machine learning regression algorithms can be managed to create a wind power forecasting model that predicts how much electricity can be produced on that day according to the varying factors; it will allow people to be less dependent on those factors. This article explores the trends for climate change and the avenues for change. It must be noted that averting one’s gaze away from this urgent issue resolves nothing.

Published in International Journal of Energy and Environmental Science (Volume 6, Issue 5)
DOI 10.11648/j.ijees.20210605.14
Page(s) 134-142
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Global Warming, Greenhouse Gas, CO2 Emissions, Renewable Energy, Wind Energy, Machine Learning Regression Algorithm

References
[1] Goethe-Universität Frankfurt am Main. (2015, May 28). Extreme global warming of the Cretaceous period punctuated with significant global cooling. ScienceDaily. Retrieved July 21, 2021 from www.sciencedaily.com/releases/2015/05/150528083818.htm.
[2] Arega Bazezew Berlie, Global Warming: A Review of the Debates on the Causes, Consequences and Politics of Global Response. Ghana Journal of Geography Vol. 10 (1), 2018 pages 144–164.
[3] Silberg, B. (2016). Why a half-degree temperature rise is a big deal – Climate Change: Vital Signs of the Planet. Retrieved 21 July 2021, from https://climate.nasa.gov/news/2458/why-a-half-degree-temperature-rise-is-a-big-deal/.
[4] Kaplan, S. (2021, July 6). Climate change has gotten deadly. It will get worse. The Washington Post. https://www.washingtonpost.com/climate-environment/2021/07/03/climate-change-heat-dome-death/.
[5] European Commission. (2017, June 28). Causes of climate change. Climate Action - European Commission. https://ec.europa.eu/clima/change/causes_en.
[6] MacMillan, A., & Turrentine, J. (2021, April 7). Global Warming 101. NRDC. https://www.nrdc.org/stories/global-warming-101#causes.
[7] United States Environmental Protection Agency. (2021, April 14). Overview-of-Greenhouse-Gases. EPA. https://www.epa.gov/ghgemissions/overview-greenhouse-gases.
[8] Panwar, N. L., Kaushik, S. C., & Kothari, S. (2011). Role of renewable energy sources in environmental protection: A review. Renewable and Sustainable Energy Reviews, 15 (3), 1513–1524. https://doi.org/10.1016/j.rser.2010.11.037.
[9] Lund, H. (2007). Renewable energy strategies for sustainable development. Energy, 32 (6), 912–919. https://doi.org/10.1016/j.energy.2006.10.017.
[10] Vezmar, S., Spajić, A., Topić, D., Šljivac, D., & Jozsa, L. (2014). Positive and negative impacts of renewable energy sources. International journal of electrical and computer engineering systems, 5 (2), 47-55.
[11] Vicedo-Cabrera, A. M., Scovronick, N., Sera, F., Royé, D., Schneider, R., Tobias, A., Astrom, C., Guo, Y., Honda, Y., Hondula, D. M., Abrutzky, R., Tong, S., Coelho, M. de, Saldiva, P. H., Lavigne, E., Correa, P. M., Ortega, N. V., Kan, H., Osorio, S., … Gasparrini, A. (2021). The burden of heat-related mortality attributable to recent human-induced climate change. Nature Climate Change, 11 (6), 492–500. https://doi.org/10.1038/s41558-021-01058-x.
[12] TWI. (n.d.). What is Renewable Energy? - Definition, Types, Benefits and Challenges. Retrieved August 2, 2021, from https://www.twi-global.com/technical-knowledge/faqs/renewable-energy.
[13] Green, E. (2021, June 2). Wind vs. Solar — Which Power Source Is Better? Elemental Green. https://elemental.green/wind-vs-solar-which-power-source-is-better/.
[14] Teixeira-Pinto, A. (2021, July 26). 2 K-nearest Neighbours Regression | Machine Learning for Biostatistics. Biostatistics Statistics Collaboration of Australia. https://bookdown.org/tpinto_home/Regression-and-Classification/k-nearest-neighbours-regression.html.
[15] scikit-learn developers. (n.d.). sklearn. neighbors. Kneighbors regressor. scikit learn. https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html.
[16] Brownlee, J. (2021, February 16). A Gentle Introduction to XGBoost for Applied Machine Learning. Machine Learning Mastery. https://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/.
[17] Grogan, M. (2020, November 7). Predicting Weekly Hotel Cancellations with XGBRegressor. Medium. https://towardsdatascience.com/predicting-weekly-hotel-cancellations-with-xgbregressor-d73eb74a8624.
[18] Brownlee, J. (2020, August 26). Train-Test split for Evaluating machine learning algorithms. Machine Learning Mastery. https://machinelearningmastery.com/train-test-split-for-evaluating-machine-learning-algorithms/.
Cite This Article
  • APA Style

    Jun Seok Hwang, Seoyeon Kim, Seunghyun Lee, Shiwoo Lee, Kenneth Chisoon Park. (2021). Wind Power Forecasting Model Through Data Analysis of Causes and Impacts of Global Warming. International Journal of Energy and Environmental Science, 6(5), 134-142. https://doi.org/10.11648/j.ijees.20210605.14

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    ACS Style

    Jun Seok Hwang; Seoyeon Kim; Seunghyun Lee; Shiwoo Lee; Kenneth Chisoon Park. Wind Power Forecasting Model Through Data Analysis of Causes and Impacts of Global Warming. Int. J. Energy Environ. Sci. 2021, 6(5), 134-142. doi: 10.11648/j.ijees.20210605.14

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    AMA Style

    Jun Seok Hwang, Seoyeon Kim, Seunghyun Lee, Shiwoo Lee, Kenneth Chisoon Park. Wind Power Forecasting Model Through Data Analysis of Causes and Impacts of Global Warming. Int J Energy Environ Sci. 2021;6(5):134-142. doi: 10.11648/j.ijees.20210605.14

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  • @article{10.11648/j.ijees.20210605.14,
      author = {Jun Seok Hwang and Seoyeon Kim and Seunghyun Lee and Shiwoo Lee and Kenneth Chisoon Park},
      title = {Wind Power Forecasting Model Through Data Analysis of Causes and Impacts of Global Warming},
      journal = {International Journal of Energy and Environmental Science},
      volume = {6},
      number = {5},
      pages = {134-142},
      doi = {10.11648/j.ijees.20210605.14},
      url = {https://doi.org/10.11648/j.ijees.20210605.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijees.20210605.14},
      abstract = {In this article, we aim to spread the need for change by informing the reader about global warming and its consequences. Global warming is a phenomenon in which average global temperatures rise due to the increase in greenhouse gases (GHG) emitted and trapped in the atmosphere. These specific gases are special in that they contribute to the flow of heat within the atmosphere as they trap more heat from the sun within the atmosphere. The most influential of these gases is CO2, which most of the data analysis in this article will be focusing on. There are many issues and problems that are attributed to global warming, along with a wide array of data that relates to the topic. By analyzing such data, one can find out the global trend in climate change and even come up with potential solutions for this increasingly threatening event. The lack of action taken by world leaders is due to general negligence and a lack of an effective and coherent solution to tackle climate change. A probable solution would be to find a surrogate to the main cause of GHG emissions, which is the substantial exploitation of conventional (nonrenewable) energy sources. Renewable energy sources are excellent substitutes to conventional energy sources and one of the most effective types is wind. This energy source is currently implemented in many areas around the globe and is producing better results than in the past when conventional energy sources were in use. Although wind turbines are more efficient than solar panels in producing electricity because they are not affected by the presence of sunlight, the amount of electricity generated can fluctuate due to various factors. To adjust to these varying conditions, machine learning regression algorithms can be managed to create a wind power forecasting model that predicts how much electricity can be produced on that day according to the varying factors; it will allow people to be less dependent on those factors. This article explores the trends for climate change and the avenues for change. It must be noted that averting one’s gaze away from this urgent issue resolves nothing.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Wind Power Forecasting Model Through Data Analysis of Causes and Impacts of Global Warming
    AU  - Jun Seok Hwang
    AU  - Seoyeon Kim
    AU  - Seunghyun Lee
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    DO  - 10.11648/j.ijees.20210605.14
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    AB  - In this article, we aim to spread the need for change by informing the reader about global warming and its consequences. Global warming is a phenomenon in which average global temperatures rise due to the increase in greenhouse gases (GHG) emitted and trapped in the atmosphere. These specific gases are special in that they contribute to the flow of heat within the atmosphere as they trap more heat from the sun within the atmosphere. The most influential of these gases is CO2, which most of the data analysis in this article will be focusing on. There are many issues and problems that are attributed to global warming, along with a wide array of data that relates to the topic. By analyzing such data, one can find out the global trend in climate change and even come up with potential solutions for this increasingly threatening event. The lack of action taken by world leaders is due to general negligence and a lack of an effective and coherent solution to tackle climate change. A probable solution would be to find a surrogate to the main cause of GHG emissions, which is the substantial exploitation of conventional (nonrenewable) energy sources. Renewable energy sources are excellent substitutes to conventional energy sources and one of the most effective types is wind. This energy source is currently implemented in many areas around the globe and is producing better results than in the past when conventional energy sources were in use. Although wind turbines are more efficient than solar panels in producing electricity because they are not affected by the presence of sunlight, the amount of electricity generated can fluctuate due to various factors. To adjust to these varying conditions, machine learning regression algorithms can be managed to create a wind power forecasting model that predicts how much electricity can be produced on that day according to the varying factors; it will allow people to be less dependent on those factors. This article explores the trends for climate change and the avenues for change. It must be noted that averting one’s gaze away from this urgent issue resolves nothing.
    VL  - 6
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    ER  - 

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Author Information
  • Williston Northampton School, Easthampton, United States

  • Branksome Hall Asia, Seogwipo-si, South Korea

  • Korean Minjok Leadership Academy (KMLA), Gangwon-do, South Korea

  • Seoul Foreign School, Seoul, South Korea

  • North London Collegiate School Jeju, Seogwipo-si, South Korea

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