Machine Learning Based Prediction of Output PV Power in India and Malaysia with the Use of Statistical Regression
Climate change and pollution are serious issues that are driving people to adopt renewable energy instead of fossil fuels. Most renewable energy technologies rely on atmospheric conditions to generate power. Solar energy is a renewable energy source that causes the least environmental damage. Solar...
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Published in | Mathematical problems in engineering Vol. 2022; pp. 1 - 10 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
New York
Hindawi
15.07.2022
Hindawi Limited |
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Abstract | Climate change and pollution are serious issues that are driving people to adopt renewable energy instead of fossil fuels. Most renewable energy technologies rely on atmospheric conditions to generate power. Solar energy is a renewable energy source that causes the least environmental damage. Solar energy can be converted to electricity, which necessitates the use of a PV system. This study presents a design, which analyses the output power performance of PV, using machine learning technique in India and Malaysia; using this, we would get the predicted amount of solar power using different weather conditions for both India and Malaysia. This study is divided into two sections, such as the data collection section and the implementation system. Dataset was collected from a weather NASA website, which took various weather parameters, based on which the model will be evaluated. The proposed research work is developed using ANN and is an amalgamation of statistical regression and neural networks, which help the model to get high accuracy by helping the model learn more complex relationships between parameters, which is able to evaluate the output power performance of photovoltaic cells with different environmental condition parameters in India and Malaysia. The ANN models are found to successfully predict PV output power with root mean square error (RMSE) of 1.5565, which was used as a measure of our model’s accuracy. This ANN model also outperforms other models available in the literature. This will have a noteworthy contribution in scaling the PV deployment in countries such as India and Malaysia and will increase the share of PV power in their national power production, as it would give the industry and the two countries an idea as to how the predicted output PV power would vary based on weather conditions, such as temperature. |
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AbstractList | Climate change and pollution are serious issues that are driving people to adopt renewable energy instead of fossil fuels. Most renewable energy technologies rely on atmospheric conditions to generate power. Solar energy is a renewable energy source that causes the least environmental damage. Solar energy can be converted to electricity, which necessitates the use of a PV system. This study presents a design, which analyses the output power performance of PV, using machine learning technique in India and Malaysia; using this, we would get the predicted amount of solar power using different weather conditions for both India and Malaysia. This study is divided into two sections, such as the data collection section and the implementation system. Dataset was collected from a weather NASA website, which took various weather parameters, based on which the model will be evaluated. The proposed research work is developed using ANN and is an amalgamation of statistical regression and neural networks, which help the model to get high accuracy by helping the model learn more complex relationships between parameters, which is able to evaluate the output power performance of photovoltaic cells with different environmental condition parameters in India and Malaysia. The ANN models are found to successfully predict PV output power with root mean square error (RMSE) of 1.5565, which was used as a measure of our model’s accuracy. This ANN model also outperforms other models available in the literature. This will have a noteworthy contribution in scaling the PV deployment in countries such as India and Malaysia and will increase the share of PV power in their national power production, as it would give the industry and the two countries an idea as to how the predicted output PV power would vary based on weather conditions, such as temperature. |
Author | Meraj, Sheikh T. Kannan, Ramani Masaoud, Ammar Yadav, Ojaswa |
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Cites_doi | 10.1109/naps.2017.8107270 10.1007/s00521-021-05915-w 10.1016/j.renene.2017.10.043 10.1016/j.jclepro.2022.132188 10.1016/j.solener.2013.10.002 10.1007/978-981-15-8606-4_3 10.1109/iccons.2018.8663110 10.1155/2021/5551014 10.3390/en12142782 10.1109/icees.2018.8442361 10.1115/1.4034823 10.1016/j.renene.2012.01.108 10.1109/pcitc.2015.7438155 10.3390/en11113231 10.1016/j.renene.2015.12.069 10.1177/0143624421994224 10.1016/j.solener.2009.05.016 10.1016/j.jclepro.2017.12.065 10.1155/2017/2437387 10.1109/intech.2016.7845051 10.1016/j.electacta.2019.135594 10.1016/j.rser.2020.109792 10.1016/j.jestch.2018.04.013 10.1109/tie.2017.2714127 |
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Copyright | Copyright © 2022 Ojaswa Yadav et al. Copyright © 2022 Ojaswa Yadav et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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SubjectTerms | Accuracy Alternative energy sources Atmospheric models Data collection Electricity distribution Emissions Energy industry Energy resources Energy technology Engineering Forecasting Forecasting techniques Fossil fuels Machine learning Mathematical models Methods Model accuracy Neural networks Parameters Photovoltaic cells Renewable energy sources Renewable resources Root-mean-square errors Software Solar energy Solar energy conversion Statistical analysis Statistical prediction Weather Websites |
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Title | Machine Learning Based Prediction of Output PV Power in India and Malaysia with the Use of Statistical Regression |
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