Wind speed prediction and energy estimation using the SARIMA method in Banyumas Regency.
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| Title: | Wind speed prediction and energy estimation using the SARIMA method in Banyumas Regency. |
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| Authors: | Yuniarto, Abdul Hakim Prima1 a.hakim.py@gmail.com, Nawangnugraeni, Devi Astri2 devi.nawangnugraeni@unsoed.ac.id, Admaja, Rafif Aldo3 rafifaldo02@gmail.com, Arsyad, Hardeka Muhammad3 hardekaarsyad156@gmail.com |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Jun2026, Vol. 16 Issue 3, p1425-1433. 9p. |
| Subjects: | Wind forecasting, Wind power, Machine learning, Renewable energy sources, Electric power consumption, Box-Jenkins forecasting |
| Abstract: | Electricity consumption in Banyumas Regency shows a significant upward trend, indicating growing energy needs across various sectors. Dependence on fossil fuels poses challenges, including environmental pollution, limited resources, and price fluctuations. As a strategic solution, developing new and renewable energy, especially wind energy, is crucial to achieving energy independence and environmental sustainability. This study aims to analyze and predict wind speed in Banyumas Regency and calculate the potential electricity production that residential-scale wind turbines can generate. The method used is the seasonal auto regressive integrated moving average (SARIMA). This study applies it within a machine learning framework, using a grid search for hyperparameter tuning, to accurately predict wind speed from historical NASA POWER data. The results show that the SARIMA (1, 0, 0)×(0, 1, 1, 52) model is the optimal model with the best prediction accuracy, as evidenced by the root mean squared error (RMSE) value of 0.516 m/s and the mean absolute error (MAE) of 0.441 m/s. Based on the model, the predicted average wind speed for the next three months is 3.41 m/s, potentially generating an average daily electricity output of 1.44 kWh. These results indicate that Banyumas Regency has promising potential for the development of small-scale wind power plants to support household energy needs or public street lighting. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194285630 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Wind speed prediction and energy estimation using the SARIMA method in Banyumas Regency. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yuniarto%2C+Abdul+Hakim+Prima%22">Yuniarto, Abdul Hakim Prima</searchLink><relatesTo>1</relatesTo><i> a.hakim.py@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Nawangnugraeni%2C+Devi+Astri%22">Nawangnugraeni, Devi Astri</searchLink><relatesTo>2</relatesTo><i> devi.nawangnugraeni@unsoed.ac.id</i><br /><searchLink fieldCode="AR" term="%22Admaja%2C+Rafif+Aldo%22">Admaja, Rafif Aldo</searchLink><relatesTo>3</relatesTo><i> rafifaldo02@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Arsyad%2C+Hardeka+Muhammad%22">Arsyad, Hardeka Muhammad</searchLink><relatesTo>3</relatesTo><i> hardekaarsyad156@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Jun2026, Vol. 16 Issue 3, p1425-1433. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Wind+forecasting%22">Wind forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+power%22">Wind power</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+consumption%22">Electric power consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Box-Jenkins+forecasting%22">Box-Jenkins forecasting</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Electricity consumption in Banyumas Regency shows a significant upward trend, indicating growing energy needs across various sectors. Dependence on fossil fuels poses challenges, including environmental pollution, limited resources, and price fluctuations. As a strategic solution, developing new and renewable energy, especially wind energy, is crucial to achieving energy independence and environmental sustainability. This study aims to analyze and predict wind speed in Banyumas Regency and calculate the potential electricity production that residential-scale wind turbines can generate. The method used is the seasonal auto regressive integrated moving average (SARIMA). This study applies it within a machine learning framework, using a grid search for hyperparameter tuning, to accurately predict wind speed from historical NASA POWER data. The results show that the SARIMA (1, 0, 0)×(0, 1, 1, 52) model is the optimal model with the best prediction accuracy, as evidenced by the root mean squared error (RMSE) value of 0.516 m/s and the mean absolute error (MAE) of 0.441 m/s. Based on the model, the predicted average wind speed for the next three months is 3.41 m/s, potentially generating an average daily electricity output of 1.44 kWh. These results indicate that Banyumas Regency has promising potential for the development of small-scale wind power plants to support household energy needs or public street lighting. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v16i3.pp1425-1433 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 1425 Subjects: – SubjectFull: Wind forecasting Type: general – SubjectFull: Wind power Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Renewable energy sources Type: general – SubjectFull: Electric power consumption Type: general – SubjectFull: Box-Jenkins forecasting Type: general Titles: – TitleFull: Wind speed prediction and energy estimation using the SARIMA method in Banyumas Regency. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yuniarto, Abdul Hakim Prima – PersonEntity: Name: NameFull: Nawangnugraeni, Devi Astri – PersonEntity: Name: NameFull: Admaja, Rafif Aldo – PersonEntity: Name: NameFull: Arsyad, Hardeka Muhammad IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 16 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
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