An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management Systems
This paper proposes an approach for accurate wind speed forecasting. While previous works have proposed approaches that have either underperformed in accuracy or were too computationally intensive, the work described in this paper was implemented using a computationally efficient model. This model p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/22/4355 |
| _version_ | 1797999833870499840 |
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| author | Saira Al-Zadjali Ahmed Al Maashri Amer Al-Hinai Sultan Al-Yahyai Mostafa Bakhtvar |
| author_facet | Saira Al-Zadjali Ahmed Al Maashri Amer Al-Hinai Sultan Al-Yahyai Mostafa Bakhtvar |
| author_sort | Saira Al-Zadjali |
| collection | DOAJ |
| description | This paper proposes an approach for accurate wind speed forecasting. While previous works have proposed approaches that have either underperformed in accuracy or were too computationally intensive, the work described in this paper was implemented using a computationally efficient model. This model provides wind speed nowcasting using a combination of perturbed observation ensemble networks and artificial neural networks. The model was validated and evaluated via simulation using data that were measured from wind masts. The simulation results show that the proposed model improved the normalized root mean square error by 20.9% compared to other contending approaches. In terms of prediction interval coverage probability, our proposed model shows a 17.8% improvement, all while using a smaller number of neural networks. Furthermore, the proposed model has an execution time that is one order of magnitude faster than other contenders. |
| first_indexed | 2024-04-11T11:11:02Z |
| format | Article |
| id | doaj.art-badb829f0d7b45cea8c66da53d512000 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T11:11:02Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-badb829f0d7b45cea8c66da53d5120002022-12-22T04:27:29ZengMDPI AGEnergies1996-10732019-11-011222435510.3390/en12224355en12224355An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management SystemsSaira Al-Zadjali0Ahmed Al Maashri1Amer Al-Hinai2Sultan Al-Yahyai3Mostafa Bakhtvar4Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, OmanElectrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, OmanElectrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, OmanInformation and Technology, Mazoon Electricity Company, Fanja 600, OmanElectrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, OmanThis paper proposes an approach for accurate wind speed forecasting. While previous works have proposed approaches that have either underperformed in accuracy or were too computationally intensive, the work described in this paper was implemented using a computationally efficient model. This model provides wind speed nowcasting using a combination of perturbed observation ensemble networks and artificial neural networks. The model was validated and evaluated via simulation using data that were measured from wind masts. The simulation results show that the proposed model improved the normalized root mean square error by 20.9% compared to other contending approaches. In terms of prediction interval coverage probability, our proposed model shows a 17.8% improvement, all while using a smaller number of neural networks. Furthermore, the proposed model has an execution time that is one order of magnitude faster than other contenders.https://www.mdpi.com/1996-1073/12/22/4355renewable energywind speed nowcastingensemble artificial neural networks |
| spellingShingle | Saira Al-Zadjali Ahmed Al Maashri Amer Al-Hinai Sultan Al-Yahyai Mostafa Bakhtvar An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management Systems Energies renewable energy wind speed nowcasting ensemble artificial neural networks |
| title | An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management Systems |
| title_full | An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management Systems |
| title_fullStr | An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management Systems |
| title_full_unstemmed | An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management Systems |
| title_short | An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management Systems |
| title_sort | accurate light weight wind speed predictor for renewable energy management systems |
| topic | renewable energy wind speed nowcasting ensemble artificial neural networks |
| url | https://www.mdpi.com/1996-1073/12/22/4355 |
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