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), 2024. Published by Science Publishing Group |
Global Warming, Greenhouse Gas, CO2 Emissions, Renewable Energy, Wind Energy, Machine Learning Regression Algorithm
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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
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
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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Download@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}
}
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 AU - Shiwoo Lee AU - Kenneth Chisoon Park Y1 - 2021/10/19 PY - 2021 N1 - https://doi.org/10.11648/j.ijees.20210605.14 DO - 10.11648/j.ijees.20210605.14 T2 - International Journal of Energy and Environmental Science JF - International Journal of Energy and Environmental Science JO - International Journal of Energy and Environmental Science SP - 134 EP - 142 PB - Science Publishing Group SN - 2578-9546 UR - https://doi.org/10.11648/j.ijees.20210605.14 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 IS - 5 ER -
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