Multivariate wind power curve modeling using multivariate adaptive regression splines and regression trees.
Wind turbine power curve (WTPC) serves as an important tool for wind turbine condition monitoring and wind power forecasting. Due to complex environmental factors and technical issues of the wind turbines, there are many outliers and inconsistencies present in the recorded data, which cannot be remo...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0290316 |
| _version_ | 1797690897922523136 |
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| author | Khurram Mushtaq Runmin Zou Asim Waris Kaifeng Yang Ji Wang Javaid Iqbal Mohammed Jameel |
| author_facet | Khurram Mushtaq Runmin Zou Asim Waris Kaifeng Yang Ji Wang Javaid Iqbal Mohammed Jameel |
| author_sort | Khurram Mushtaq |
| collection | DOAJ |
| description | Wind turbine power curve (WTPC) serves as an important tool for wind turbine condition monitoring and wind power forecasting. Due to complex environmental factors and technical issues of the wind turbines, there are many outliers and inconsistencies present in the recorded data, which cannot be removed through any pre-processing technique. However, the current WTPC models have limited ability to understand such complex relation between wind speed and wind power and have limited non-linear fitting ability, which limit their modelling accuracy. In this paper, the accuracy of the WTPC models is improved in two ways: first is by developing multivariate models and second is by proposing MARS as WTPC modeling technique. MARS is a regression-based flexible modeling technique that automatically models complex the nonlinearities in the data using spline functions. Experimental results show that by incorporating additional inputs the accuracy of the power curve estimation is significantly improved. Also by studying the error distribution it is proved that multivariate models successfully mitigate the adverse effect of hidden outliers, as their distribution has higher peaks and lesser standard deviation, which proves that the errors, are more converged to zero compared to the univariate models. Additionally, MARS with its superior non-linear fitting ability outperforms the compared methods in terms of the error metrics and ranks higher than regression trees and several other popular parametric and non-parametric methods. Finally, an outlier detection method is developed to remove the hidden outliers from the data using the error distribution of the modeled power curves. |
| first_indexed | 2024-03-12T02:05:44Z |
| format | Article |
| id | doaj.art-f834f77f61a34f7baec5d66c8b9d0b7a |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-12T02:05:44Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f834f77f61a34f7baec5d66c8b9d0b7a2023-09-07T05:31:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01188e029031610.1371/journal.pone.0290316Multivariate wind power curve modeling using multivariate adaptive regression splines and regression trees.Khurram MushtaqRunmin ZouAsim WarisKaifeng YangJi WangJavaid IqbalMohammed JameelWind turbine power curve (WTPC) serves as an important tool for wind turbine condition monitoring and wind power forecasting. Due to complex environmental factors and technical issues of the wind turbines, there are many outliers and inconsistencies present in the recorded data, which cannot be removed through any pre-processing technique. However, the current WTPC models have limited ability to understand such complex relation between wind speed and wind power and have limited non-linear fitting ability, which limit their modelling accuracy. In this paper, the accuracy of the WTPC models is improved in two ways: first is by developing multivariate models and second is by proposing MARS as WTPC modeling technique. MARS is a regression-based flexible modeling technique that automatically models complex the nonlinearities in the data using spline functions. Experimental results show that by incorporating additional inputs the accuracy of the power curve estimation is significantly improved. Also by studying the error distribution it is proved that multivariate models successfully mitigate the adverse effect of hidden outliers, as their distribution has higher peaks and lesser standard deviation, which proves that the errors, are more converged to zero compared to the univariate models. Additionally, MARS with its superior non-linear fitting ability outperforms the compared methods in terms of the error metrics and ranks higher than regression trees and several other popular parametric and non-parametric methods. Finally, an outlier detection method is developed to remove the hidden outliers from the data using the error distribution of the modeled power curves.https://doi.org/10.1371/journal.pone.0290316 |
| spellingShingle | Khurram Mushtaq Runmin Zou Asim Waris Kaifeng Yang Ji Wang Javaid Iqbal Mohammed Jameel Multivariate wind power curve modeling using multivariate adaptive regression splines and regression trees. PLoS ONE |
| title | Multivariate wind power curve modeling using multivariate adaptive regression splines and regression trees. |
| title_full | Multivariate wind power curve modeling using multivariate adaptive regression splines and regression trees. |
| title_fullStr | Multivariate wind power curve modeling using multivariate adaptive regression splines and regression trees. |
| title_full_unstemmed | Multivariate wind power curve modeling using multivariate adaptive regression splines and regression trees. |
| title_short | Multivariate wind power curve modeling using multivariate adaptive regression splines and regression trees. |
| title_sort | multivariate wind power curve modeling using multivariate adaptive regression splines and regression trees |
| url | https://doi.org/10.1371/journal.pone.0290316 |
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