Prediction of Monthly Wind Velocity Using Machine Learning
The utilization of non-renewable energy resources necessitates the power sector's adoption of alternative energy sources, including photovoltaic and wind power generation systems. This academic investigation utilizes two machine learning methodologies, in particular, the study utilizes the rand...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00107.pdf |
| _version_ | 1797213262655258624 |
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| author | Al-Hasani Ahmed T. Jaber Ednan Al-Juburi Ban Hussein Hasan Fouad Ramadhan Ali J. Oluwaseun Adebayo Adelaja |
| author_facet | Al-Hasani Ahmed T. Jaber Ednan Al-Juburi Ban Hussein Hasan Fouad Ramadhan Ali J. Oluwaseun Adebayo Adelaja |
| author_sort | Al-Hasani Ahmed T. |
| collection | DOAJ |
| description | The utilization of non-renewable energy resources necessitates the power sector's adoption of alternative energy sources, including photovoltaic and wind power generation systems. This academic investigation utilizes two machine learning methodologies, in particular, the study utilizes the random forest and support vector machine algorithms. to conduct its analyses. predict the velocity of the wind in the Diyala governorate of Iraq for the subsequent time interval. This is achieved solely by utilizing historical monthly time series data as input predictors. The three performance metrics employed encompass the coefficient of assurance (R2), root cruel square mistake (RMSE), and cruel outright blunder (MAE). The findings demonstrate that utilizing a lag of 12 months in the time series data (the maximum lag duration tested) as input predictors leads to the most accurate predictions in terms of performance. However, the prediction performance of the two algorithms used was almost similar (RF's RMSE, MAE, and R2 were 0.237, 0.180, and 0.836, while for SVM were 0.223, 0.171, and 0.856). The capacity to anticipate wind speed constitutes a paramount advantage to Iraq, given its current predicament in the electric power industry, and this has the potential to enable stakeholders to forecast oversupply or undersupply and implement pre-emptive measures. |
| first_indexed | 2024-04-24T10:55:29Z |
| format | Article |
| id | doaj.art-87ca4491bb10448f8d371aa344feb538 |
| institution | Directory Open Access Journal |
| issn | 2117-4458 |
| language | English |
| last_indexed | 2024-04-24T10:55:29Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj.art-87ca4491bb10448f8d371aa344feb5382024-04-12T07:36:22ZengEDP SciencesBIO Web of Conferences2117-44582024-01-01970010710.1051/bioconf/20249700107bioconf_iscku2024_00107Prediction of Monthly Wind Velocity Using Machine LearningAl-Hasani Ahmed T.0Jaber Ednan Al-Juburi Ban1Hussein Hasan Fouad2Ramadhan Ali J.3Oluwaseun Adebayo Adelaja4AL-Furat Al-Awsat Technical UniversityGeneral Directorate for Education in NajafAL-Furat Al-Awsat Technical UniversityUniversity of AlkafeelLagos State UniversityThe utilization of non-renewable energy resources necessitates the power sector's adoption of alternative energy sources, including photovoltaic and wind power generation systems. This academic investigation utilizes two machine learning methodologies, in particular, the study utilizes the random forest and support vector machine algorithms. to conduct its analyses. predict the velocity of the wind in the Diyala governorate of Iraq for the subsequent time interval. This is achieved solely by utilizing historical monthly time series data as input predictors. The three performance metrics employed encompass the coefficient of assurance (R2), root cruel square mistake (RMSE), and cruel outright blunder (MAE). The findings demonstrate that utilizing a lag of 12 months in the time series data (the maximum lag duration tested) as input predictors leads to the most accurate predictions in terms of performance. However, the prediction performance of the two algorithms used was almost similar (RF's RMSE, MAE, and R2 were 0.237, 0.180, and 0.836, while for SVM were 0.223, 0.171, and 0.856). The capacity to anticipate wind speed constitutes a paramount advantage to Iraq, given its current predicament in the electric power industry, and this has the potential to enable stakeholders to forecast oversupply or undersupply and implement pre-emptive measures.https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00107.pdf |
| spellingShingle | Al-Hasani Ahmed T. Jaber Ednan Al-Juburi Ban Hussein Hasan Fouad Ramadhan Ali J. Oluwaseun Adebayo Adelaja Prediction of Monthly Wind Velocity Using Machine Learning BIO Web of Conferences |
| title | Prediction of Monthly Wind Velocity Using Machine Learning |
| title_full | Prediction of Monthly Wind Velocity Using Machine Learning |
| title_fullStr | Prediction of Monthly Wind Velocity Using Machine Learning |
| title_full_unstemmed | Prediction of Monthly Wind Velocity Using Machine Learning |
| title_short | Prediction of Monthly Wind Velocity Using Machine Learning |
| title_sort | prediction of monthly wind velocity using machine learning |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2024/16/bioconf_iscku2024_00107.pdf |
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