Optimal Placement of Wind Power System Using Machine Learning
The switch from fossil fuels to sustainable energy sources is crucial for power generation sustainability. Hence, this study, proposed a plan for the installation of wind turbines in Doha Qatar, and forecasted the future temperature and wind speed for the optimal placement of large-scale wind turbi...
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| Format: | Article |
| Language: | English |
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Sir Syed University of Engineering and Technology, Karachi.
2025-06-01
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| Series: | Sir Syed University Research Journal of Engineering and Technology |
| Online Access: | https://sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/679 |
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| _version_ | 1849428752620060672 |
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| author | Abdul Karim Muhammad Amir Raza Darakhshan Ara Muhammad Shahid Shakir Ali Soomro |
| author_facet | Abdul Karim Muhammad Amir Raza Darakhshan Ara Muhammad Shahid Shakir Ali Soomro |
| author_sort | Abdul Karim |
| collection | DOAJ |
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The switch from fossil fuels to sustainable energy sources is crucial for power generation sustainability. Hence, this study, proposed a plan for the installation of wind turbines in Doha Qatar, and forecasted the future temperature and wind speed for the optimal placement of large-scale wind turbines using the Pythons algorithms namely, Long Short-Term Memory (LSTM), Prophet (PT), Support Vector Regression (SVR), Linear Regression (LR), Seasonal Autoregressive Integrated Moving Average with External Factors (SARIMAX), and K-Nearest Neighbors (KNN). These models have taken raw data from 2000 to 2019 and tested from 2020 to 2023 and finally predict the future from 2023 to 2030. The results show that for renewable energy variables such as temperature and wind speed, statistical models such as SARIMAX perform better than traditional models such as LSTM, PT, SVR, LR, and KNN. Because SARIMAX captures long-term dependencies, it is particularly suitable for time series data where past events have a large impact on future values. It is recommended that wind power has enormous potential and offers a low-carbon future for Doha Qatar.
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| format | Article |
| id | doaj-art-077b20d466da49259fce7b4e07f6da56 |
| institution | Kabale University |
| issn | 1997-0641 2415-2048 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Sir Syed University of Engineering and Technology, Karachi. |
| record_format | Article |
| series | Sir Syed University Research Journal of Engineering and Technology |
| spelling | doaj-art-077b20d466da49259fce7b4e07f6da562025-08-20T03:28:37ZengSir Syed University of Engineering and Technology, Karachi.Sir Syed University Research Journal of Engineering and Technology1997-06412415-20482025-06-0115110.33317/ssurj.679Optimal Placement of Wind Power System Using Machine LearningAbdul Karim0Muhammad Amir Raza1Darakhshan Ara2Muhammad Shahid3Shakir Ali Soomro4Department of Electrical Engineering Mehran University of Engineering and Technology, SZAB Campus Khairpur Mirs’ Sindh PakistanMUETDepartment of Basic Sciences and Humanities, Dawood University of Engineering and Technology. Karachi, PakistanDepartment of Electronic Engineering, Dawood University of Engineering and Technology, Karachi, PakistanDepartment of Electrical Engineering Mehran University of Engineering and Technology, SZAB Campus Khairpur Mirs’ Sindh Pakistan The switch from fossil fuels to sustainable energy sources is crucial for power generation sustainability. Hence, this study, proposed a plan for the installation of wind turbines in Doha Qatar, and forecasted the future temperature and wind speed for the optimal placement of large-scale wind turbines using the Pythons algorithms namely, Long Short-Term Memory (LSTM), Prophet (PT), Support Vector Regression (SVR), Linear Regression (LR), Seasonal Autoregressive Integrated Moving Average with External Factors (SARIMAX), and K-Nearest Neighbors (KNN). These models have taken raw data from 2000 to 2019 and tested from 2020 to 2023 and finally predict the future from 2023 to 2030. The results show that for renewable energy variables such as temperature and wind speed, statistical models such as SARIMAX perform better than traditional models such as LSTM, PT, SVR, LR, and KNN. Because SARIMAX captures long-term dependencies, it is particularly suitable for time series data where past events have a large impact on future values. It is recommended that wind power has enormous potential and offers a low-carbon future for Doha Qatar. https://sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/679 |
| spellingShingle | Abdul Karim Muhammad Amir Raza Darakhshan Ara Muhammad Shahid Shakir Ali Soomro Optimal Placement of Wind Power System Using Machine Learning Sir Syed University Research Journal of Engineering and Technology |
| title | Optimal Placement of Wind Power System Using Machine Learning |
| title_full | Optimal Placement of Wind Power System Using Machine Learning |
| title_fullStr | Optimal Placement of Wind Power System Using Machine Learning |
| title_full_unstemmed | Optimal Placement of Wind Power System Using Machine Learning |
| title_short | Optimal Placement of Wind Power System Using Machine Learning |
| title_sort | optimal placement of wind power system using machine learning |
| url | https://sirsyeduniversity.edu.pk/ssurj/rj/index.php/ssurj/article/view/679 |
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