Solar and Wind Data Recognition: Fourier Regression for Robust Recovery
Accurate prediction of renewable energy output is essential for integrating sustainable energy sources into the grid, facilitating a transition towards a more resilient energy infrastructure. Novel applications of machine learning and artificial intelligence are being leveraged to enhance forecastin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/8/3/23 |
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| author | Abdullah F. Al-Aboosi Aldo Jonathan Muñoz Vazquez Fadhil Y. Al-Aboosi Mahmoud El-Halwagi Wei Zhan |
| author_facet | Abdullah F. Al-Aboosi Aldo Jonathan Muñoz Vazquez Fadhil Y. Al-Aboosi Mahmoud El-Halwagi Wei Zhan |
| author_sort | Abdullah F. Al-Aboosi |
| collection | DOAJ |
| description | Accurate prediction of renewable energy output is essential for integrating sustainable energy sources into the grid, facilitating a transition towards a more resilient energy infrastructure. Novel applications of machine learning and artificial intelligence are being leveraged to enhance forecasting methodologies, enabling more accurate predictions and optimized decision-making capabilities. Integrating these novel paradigms improves forecasting accuracy, fostering a more efficient and reliable energy grid. These advancements allow better demand management, optimize resource allocation, and improve robustness to potential disruptions. The data collected from solar intensity and wind speed is often recorded through sensor-equipped instruments, which may encounter intermittent or permanent faults. Hence, this paper proposes a novel Fourier network regression model to process solar irradiance and wind speed data. The proposed approach enables accurate prediction of the underlying smooth components, facilitating effective reconstruction of missing data and enhancing the overall forecasting performance. The present study focuses on Midland, Texas, as a case study to assess direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and wind speed. Remarkably, the model exhibits a correlation of 1 with a minimal RMSE (root mean square error) of 0.0007555. This study leverages Fourier analysis for renewable energy applications, with the aim of establishing a methodology that can be applied to a novel geographic context. |
| first_indexed | 2024-04-24T18:33:41Z |
| format | Article |
| id | doaj.art-b935293f8b654c849a13e27141b405d2 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-04-24T18:33:41Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-b935293f8b654c849a13e27141b405d22024-03-27T13:21:18ZengMDPI AGBig Data and Cognitive Computing2504-22892024-02-01832310.3390/bdcc8030023Solar and Wind Data Recognition: Fourier Regression for Robust RecoveryAbdullah F. Al-Aboosi0Aldo Jonathan Muñoz Vazquez1Fadhil Y. Al-Aboosi2Mahmoud El-Halwagi3Wei Zhan4Department of Multidisciplinary Engineering, Texas A&M University, College Station, TX 77843, USADepartment of Multidisciplinary Engineering, Texas A&M University, McAllen, TX 78504, USARAPID Manufacturing Institute-AIChE, New York, NY 10005, USAThe Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USADepartment of Engineering Technology and Industrial Distribution, Texas A&M University, College Station, TX 77843, USAAccurate prediction of renewable energy output is essential for integrating sustainable energy sources into the grid, facilitating a transition towards a more resilient energy infrastructure. Novel applications of machine learning and artificial intelligence are being leveraged to enhance forecasting methodologies, enabling more accurate predictions and optimized decision-making capabilities. Integrating these novel paradigms improves forecasting accuracy, fostering a more efficient and reliable energy grid. These advancements allow better demand management, optimize resource allocation, and improve robustness to potential disruptions. The data collected from solar intensity and wind speed is often recorded through sensor-equipped instruments, which may encounter intermittent or permanent faults. Hence, this paper proposes a novel Fourier network regression model to process solar irradiance and wind speed data. The proposed approach enables accurate prediction of the underlying smooth components, facilitating effective reconstruction of missing data and enhancing the overall forecasting performance. The present study focuses on Midland, Texas, as a case study to assess direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and wind speed. Remarkably, the model exhibits a correlation of 1 with a minimal RMSE (root mean square error) of 0.0007555. This study leverages Fourier analysis for renewable energy applications, with the aim of establishing a methodology that can be applied to a novel geographic context.https://www.mdpi.com/2504-2289/8/3/23wind speedDNIDHIregressionpredictiondata analysis |
| spellingShingle | Abdullah F. Al-Aboosi Aldo Jonathan Muñoz Vazquez Fadhil Y. Al-Aboosi Mahmoud El-Halwagi Wei Zhan Solar and Wind Data Recognition: Fourier Regression for Robust Recovery Big Data and Cognitive Computing wind speed DNI DHI regression prediction data analysis |
| title | Solar and Wind Data Recognition: Fourier Regression for Robust Recovery |
| title_full | Solar and Wind Data Recognition: Fourier Regression for Robust Recovery |
| title_fullStr | Solar and Wind Data Recognition: Fourier Regression for Robust Recovery |
| title_full_unstemmed | Solar and Wind Data Recognition: Fourier Regression for Robust Recovery |
| title_short | Solar and Wind Data Recognition: Fourier Regression for Robust Recovery |
| title_sort | solar and wind data recognition fourier regression for robust recovery |
| topic | wind speed DNI DHI regression prediction data analysis |
| url | https://www.mdpi.com/2504-2289/8/3/23 |
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