A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems
To plan operations and avoid any grid disturbances, power utilities require accurate power generation estimates for renewable generation. The generation estimates for wind power stations require an accurate prediction of wind speed and direction. This paper proposes a new prediction model for nowcas...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/23/7878 |
| _version_ | 1827674994175377408 |
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| author | Saira Al-Zadjali Ahmed Al Maashri Amer Al-Hinai Rashid Al Abri Swaroop Gajare Sultan Al Yahyai Mostafa Bakhtvar |
| author_facet | Saira Al-Zadjali Ahmed Al Maashri Amer Al-Hinai Rashid Al Abri Swaroop Gajare Sultan Al Yahyai Mostafa Bakhtvar |
| author_sort | Saira Al-Zadjali |
| collection | DOAJ |
| description | To plan operations and avoid any grid disturbances, power utilities require accurate power generation estimates for renewable generation. The generation estimates for wind power stations require an accurate prediction of wind speed and direction. This paper proposes a new prediction model for nowcasting the wind speed and direction, which can be used to predict the output of a wind power plant. The proposed model uses perturbed observations to train the ensemble networks. The trained model is then used to predict the wind speed and direction. The paper performs a comparative assessment of three artificial neural network models. It also studies the performance of introducing perturbed observations to the model using six different interpolation techniques. For each technique, the computational efficiency is measured and assessed. Furthermore, the paper presents an exhaustive investigation of the performance of neural network types and several techniques in training, data splitting, and interpolation. To check the efficacy of the proposed model, the power output from a real wind farm is predicted and compared with the actual recorded measurements. The results of the comprehensive analysis show that the proposed model outperforms contending models in terms of accuracy and execution time. Therefore, this model can be used by operators to reliably generate a dispatch plan. |
| first_indexed | 2024-03-10T04:54:59Z |
| format | Article |
| id | doaj.art-16ba20779ae64accb3061e70df65d245 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T04:54:59Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-16ba20779ae64accb3061e70df65d2452023-11-23T02:19:12ZengMDPI AGEnergies1996-10732021-11-011423787810.3390/en14237878A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management SystemsSaira Al-Zadjali0Ahmed Al Maashri1Amer Al-Hinai2Rashid Al Abri3Swaroop Gajare4Sultan Al Yahyai5Mostafa Bakhtvar6Department of Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, OmanDepartment of Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, OmanDepartment of Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, OmanDepartment of Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, OmanSustainable Energy Research Center, Sultan Qaboos University, Al Khodh 123, OmanInformation and Technology, Mazoon Electricity Company, Fanja 600, OmanSustainable Energy Research Center, Sultan Qaboos University, Al Khodh 123, OmanTo plan operations and avoid any grid disturbances, power utilities require accurate power generation estimates for renewable generation. The generation estimates for wind power stations require an accurate prediction of wind speed and direction. This paper proposes a new prediction model for nowcasting the wind speed and direction, which can be used to predict the output of a wind power plant. The proposed model uses perturbed observations to train the ensemble networks. The trained model is then used to predict the wind speed and direction. The paper performs a comparative assessment of three artificial neural network models. It also studies the performance of introducing perturbed observations to the model using six different interpolation techniques. For each technique, the computational efficiency is measured and assessed. Furthermore, the paper presents an exhaustive investigation of the performance of neural network types and several techniques in training, data splitting, and interpolation. To check the efficacy of the proposed model, the power output from a real wind farm is predicted and compared with the actual recorded measurements. The results of the comprehensive analysis show that the proposed model outperforms contending models in terms of accuracy and execution time. Therefore, this model can be used by operators to reliably generate a dispatch plan.https://www.mdpi.com/1996-1073/14/23/7878ensemble neural networksnowcastingrenewable energywind direction predictionwind speed prediction |
| spellingShingle | Saira Al-Zadjali Ahmed Al Maashri Amer Al-Hinai Rashid Al Abri Swaroop Gajare Sultan Al Yahyai Mostafa Bakhtvar A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems Energies ensemble neural networks nowcasting renewable energy wind direction prediction wind speed prediction |
| title | A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems |
| title_full | A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems |
| title_fullStr | A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems |
| title_full_unstemmed | A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems |
| title_short | A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems |
| title_sort | fast and accurate wind speed and direction nowcasting model for renewable energy management systems |
| topic | ensemble neural networks nowcasting renewable energy wind direction prediction wind speed prediction |
| url | https://www.mdpi.com/1996-1073/14/23/7878 |
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