Wind Speed Predictability Accuracy with Height Using LiDAR Based Measurements and Artificial Neural Networks
Accurate prediction of future wind speed is important for wind energy integration into the power grid. Wind speeds are usually measured and predicted at lower heights, while modern wind turbines have hub heights of about 80–120 m. As per understanding, this is first attempt to analyze predictability...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-07-01
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| Series: | Applied Artificial Intelligence |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/08839514.2021.1922850 |
| _version_ | 1797684868648271872 |
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| author | M. Mohandes S. Rehman H. Nuha M.S. Islam F.H. Schulze |
| author_facet | M. Mohandes S. Rehman H. Nuha M.S. Islam F.H. Schulze |
| author_sort | M. Mohandes |
| collection | DOAJ |
| description | Accurate prediction of future wind speed is important for wind energy integration into the power grid. Wind speeds are usually measured and predicted at lower heights, while modern wind turbines have hub heights of about 80–120 m. As per understanding, this is first attempt to analyze predictability of wind speed with height. To achieve this, wind data was collected using Laser Illuminated Detection and Ranging (LiDAR) system at 10 m, 20 m, 40 m, 90 m, 120 m, 200 m, 250 m and 300 m heights. The collected data is used for training and testing an Artificial Neural Network (ANN) for hourly wind speed prediction for each of the future 12 hours, using 48 previous hourly values. Careful analyses of short term wind speed prediction at different heights and future hours show that wind speed is predicted more accurately at higher heights. For example, the mean absolute percent error decreases from 0.25 to 0.12 corresponding to heights 10 to 300 m, respectively for the 6th future hour prediction, an improvement of around 50%. The performance of ANN method is compared with hybrid genetic algorithm and ANN method namely GANN. Results showed that GANN outperformed ANN for most of the heights for prediction of wind speed at the future 6th hour. Results are also confirmed on another data set and other methods. |
| first_indexed | 2024-03-12T00:35:59Z |
| format | Article |
| id | doaj.art-3bb42f131d0c4870854b95ce00a7ca23 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:35:59Z |
| publishDate | 2021-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-3bb42f131d0c4870854b95ce00a7ca232023-09-15T09:33:58ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452021-07-0135860562210.1080/08839514.2021.19228501922850Wind Speed Predictability Accuracy with Height Using LiDAR Based Measurements and Artificial Neural NetworksM. Mohandes0S. Rehman1H. Nuha2M.S. Islam3F.H. Schulze4King Fahd University of Petroleum & MineralsResearch Institute, King Fahd University of Petroleum & MineralsTelkom UniversityKing Fahd University of Petroleum & MineralsCESI Middle EastAccurate prediction of future wind speed is important for wind energy integration into the power grid. Wind speeds are usually measured and predicted at lower heights, while modern wind turbines have hub heights of about 80–120 m. As per understanding, this is first attempt to analyze predictability of wind speed with height. To achieve this, wind data was collected using Laser Illuminated Detection and Ranging (LiDAR) system at 10 m, 20 m, 40 m, 90 m, 120 m, 200 m, 250 m and 300 m heights. The collected data is used for training and testing an Artificial Neural Network (ANN) for hourly wind speed prediction for each of the future 12 hours, using 48 previous hourly values. Careful analyses of short term wind speed prediction at different heights and future hours show that wind speed is predicted more accurately at higher heights. For example, the mean absolute percent error decreases from 0.25 to 0.12 corresponding to heights 10 to 300 m, respectively for the 6th future hour prediction, an improvement of around 50%. The performance of ANN method is compared with hybrid genetic algorithm and ANN method namely GANN. Results showed that GANN outperformed ANN for most of the heights for prediction of wind speed at the future 6th hour. Results are also confirmed on another data set and other methods.http://dx.doi.org/10.1080/08839514.2021.1922850neural networks |
| spellingShingle | M. Mohandes S. Rehman H. Nuha M.S. Islam F.H. Schulze Wind Speed Predictability Accuracy with Height Using LiDAR Based Measurements and Artificial Neural Networks Applied Artificial Intelligence neural networks |
| title | Wind Speed Predictability Accuracy with Height Using LiDAR Based Measurements and Artificial Neural Networks |
| title_full | Wind Speed Predictability Accuracy with Height Using LiDAR Based Measurements and Artificial Neural Networks |
| title_fullStr | Wind Speed Predictability Accuracy with Height Using LiDAR Based Measurements and Artificial Neural Networks |
| title_full_unstemmed | Wind Speed Predictability Accuracy with Height Using LiDAR Based Measurements and Artificial Neural Networks |
| title_short | Wind Speed Predictability Accuracy with Height Using LiDAR Based Measurements and Artificial Neural Networks |
| title_sort | wind speed predictability accuracy with height using lidar based measurements and artificial neural networks |
| topic | neural networks |
| url | http://dx.doi.org/10.1080/08839514.2021.1922850 |
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