A novel energy pattern factor-based optimized approach for assessing Weibull parameters for wind power applications
Abstract One of the green, clean, and environment-friendly sources of energy is wind energy. For the assessment of wind energy potential, the parameters of the probability distribution function (PDF), i.e., Weibull distribution (WD), that fits well with the wind speed data must be known. In this res...
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| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80929-7 |
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| author | Ghulam Abbas Arshad Ali Mohamed Tahar Ben Othman Muhammad Wasim Nawaz Ateeq Ur Rehman Habib Hamam |
| author_facet | Ghulam Abbas Arshad Ali Mohamed Tahar Ben Othman Muhammad Wasim Nawaz Ateeq Ur Rehman Habib Hamam |
| author_sort | Ghulam Abbas |
| collection | DOAJ |
| description | Abstract One of the green, clean, and environment-friendly sources of energy is wind energy. For the assessment of wind energy potential, the parameters of the probability distribution function (PDF), i.e., Weibull distribution (WD), that fits well with the wind speed data must be known. In this research, we proposed a novel optimized energy pattern factor method (NOEPFM) based on the trust-region-dogleg algorithm and applied it to wind speed data of four cities of the Southern region of Punjab, Pakistan, to determine WD parameters, i.e., shape k and scale c parameters. In order to authenticate the practicability of the proposed NOEPFM, it is compared with the other existing energy pattern factor (EPF)-based methods such as the energy pattern factor method (EPFM), Sathyajith’s EPFM (EPFMS), and novel EPFM (NEPFM). The performance of NOEPFM is measured in terms of five goodness-of-fit indices, namely root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), coefficient of efficiency (CoE), and maximum absolute error (MaxAE). Numerical results reveal that the NOEPFM method was the best fit compared to the other EPFMs for all the considered wind speed datasets. This justifies the workability of the proposed NOEPFM and can serve as an enhanced approach for calculating wind power potential. |
| format | Article |
| id | doaj-art-813593b100494757b73705c8f363f7f9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-813593b100494757b73705c8f363f7f92025-01-05T12:20:23ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-80929-7A novel energy pattern factor-based optimized approach for assessing Weibull parameters for wind power applicationsGhulam Abbas0Arshad Ali1Mohamed Tahar Ben Othman2Muhammad Wasim Nawaz3Ateeq Ur Rehman4Habib Hamam5Department of Electrical Engineering, The University of LahoreFaculty of Computer and Information Systems, Islamic University of MadinahDepartment of Computer Science, College of Computer, Qassim UniversityDepartment of Computer Engineering, The University of LahoreSchool of Computing, Gachon UniversityFaculty of Engineering, Uni de MonctonAbstract One of the green, clean, and environment-friendly sources of energy is wind energy. For the assessment of wind energy potential, the parameters of the probability distribution function (PDF), i.e., Weibull distribution (WD), that fits well with the wind speed data must be known. In this research, we proposed a novel optimized energy pattern factor method (NOEPFM) based on the trust-region-dogleg algorithm and applied it to wind speed data of four cities of the Southern region of Punjab, Pakistan, to determine WD parameters, i.e., shape k and scale c parameters. In order to authenticate the practicability of the proposed NOEPFM, it is compared with the other existing energy pattern factor (EPF)-based methods such as the energy pattern factor method (EPFM), Sathyajith’s EPFM (EPFMS), and novel EPFM (NEPFM). The performance of NOEPFM is measured in terms of five goodness-of-fit indices, namely root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), coefficient of efficiency (CoE), and maximum absolute error (MaxAE). Numerical results reveal that the NOEPFM method was the best fit compared to the other EPFMs for all the considered wind speed datasets. This justifies the workability of the proposed NOEPFM and can serve as an enhanced approach for calculating wind power potential.https://doi.org/10.1038/s41598-024-80929-7Energy Pattern Factor (EPF)Energy EfficiencyNOEPFMOptimizationWeibull ParametersStatistical Indicators |
| spellingShingle | Ghulam Abbas Arshad Ali Mohamed Tahar Ben Othman Muhammad Wasim Nawaz Ateeq Ur Rehman Habib Hamam A novel energy pattern factor-based optimized approach for assessing Weibull parameters for wind power applications Scientific Reports Energy Pattern Factor (EPF) Energy Efficiency NOEPFM Optimization Weibull Parameters Statistical Indicators |
| title | A novel energy pattern factor-based optimized approach for assessing Weibull parameters for wind power applications |
| title_full | A novel energy pattern factor-based optimized approach for assessing Weibull parameters for wind power applications |
| title_fullStr | A novel energy pattern factor-based optimized approach for assessing Weibull parameters for wind power applications |
| title_full_unstemmed | A novel energy pattern factor-based optimized approach for assessing Weibull parameters for wind power applications |
| title_short | A novel energy pattern factor-based optimized approach for assessing Weibull parameters for wind power applications |
| title_sort | novel energy pattern factor based optimized approach for assessing weibull parameters for wind power applications |
| topic | Energy Pattern Factor (EPF) Energy Efficiency NOEPFM Optimization Weibull Parameters Statistical Indicators |
| url | https://doi.org/10.1038/s41598-024-80929-7 |
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