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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/

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.

Details

Title
Machine Learning Based Prediction of Output PV Power in India and Malaysia with the Use of Statistical Regression
Author
Yadav, Ojaswa 1   VIAFID ORCID Logo  ; Kannan, Ramani 1   VIAFID ORCID Logo  ; Meraj, Sheikh T 1 ; Masaoud, Ammar 2   VIAFID ORCID Logo 

 Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar32610, Perak, Malaysia 
 Faculty of Mechanical and Electrical Engineering, Al Baath University, Homs 22743, Syria 
Editor
Ramin Ranjbarzadeh
Publication year
2022
Publication date
2022
Publisher
John Wiley & Sons, Inc.
ISSN
1024123X
e-ISSN
15635147
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2693566301
Copyright
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/