Scenario-based wind speed estimation using a new hybrid metaheuristic model: Particle swarm optimization and radial movement optimization
This paper presents a new hybrid metaheuristic model in order to estimate wind speeds accurately. The study was started by the training process of artificial neural networks with some metaheuristic algorithms such as evolutionary strategy, genetic algorithm, ant colony optimization, probability-base...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2019-06-01
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| Series: | Measurement + Control |
| Online Access: | https://doi.org/10.1177/0020294019842597 |
| _version_ | 1818203748055384064 |
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| author | Alper Kerem Ali Saygin |
| author_facet | Alper Kerem Ali Saygin |
| author_sort | Alper Kerem |
| collection | DOAJ |
| description | This paper presents a new hybrid metaheuristic model in order to estimate wind speeds accurately. The study was started by the training process of artificial neural networks with some metaheuristic algorithms such as evolutionary strategy, genetic algorithm, ant colony optimization, probability-based incremental learning, particle swarm optimization, and radial movement optimization in the literature. The success of each model is recorded in graphs. In order to make the closest estimation and to increase the system stability, a new hybrid metaheuristic model was developed using particle swarm optimization and radial movement optimization, and the training process of artificial neural networks was performed with this new model. The data were obtained by real-time measurements from a 63-m-high wind measurement station built at the coordinates of UTM E 263254 and N 4173479, altitude 1313 m. Two different scenarios were created using actual data and applied to all models. It was observed that the error values in the designed new hybrid metaheuristic model were lower than those of the other models. |
| first_indexed | 2024-12-12T03:30:16Z |
| format | Article |
| id | doaj.art-b853fdf68d944a1192bc89ce00d9cf46 |
| institution | Directory Open Access Journal |
| issn | 0020-2940 |
| language | English |
| last_indexed | 2024-12-12T03:30:16Z |
| publishDate | 2019-06-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Measurement + Control |
| spelling | doaj.art-b853fdf68d944a1192bc89ce00d9cf462022-12-22T00:39:56ZengSAGE PublishingMeasurement + Control0020-29402019-06-015210.1177/0020294019842597Scenario-based wind speed estimation using a new hybrid metaheuristic model: Particle swarm optimization and radial movement optimizationAlper Kerem0Ali Saygin1Department of Electrical-Electronics Engineering, Faculty of Engineering and Architecture, Kahramanmaras Sutcu Imam University, Kahramanmaras, TurkeyDepartment of Electrical and Electronics Engineering, Faculty of Technology, Gazi University, Ankara, TurkeyThis paper presents a new hybrid metaheuristic model in order to estimate wind speeds accurately. The study was started by the training process of artificial neural networks with some metaheuristic algorithms such as evolutionary strategy, genetic algorithm, ant colony optimization, probability-based incremental learning, particle swarm optimization, and radial movement optimization in the literature. The success of each model is recorded in graphs. In order to make the closest estimation and to increase the system stability, a new hybrid metaheuristic model was developed using particle swarm optimization and radial movement optimization, and the training process of artificial neural networks was performed with this new model. The data were obtained by real-time measurements from a 63-m-high wind measurement station built at the coordinates of UTM E 263254 and N 4173479, altitude 1313 m. Two different scenarios were created using actual data and applied to all models. It was observed that the error values in the designed new hybrid metaheuristic model were lower than those of the other models.https://doi.org/10.1177/0020294019842597 |
| spellingShingle | Alper Kerem Ali Saygin Scenario-based wind speed estimation using a new hybrid metaheuristic model: Particle swarm optimization and radial movement optimization Measurement + Control |
| title | Scenario-based wind speed estimation using a new hybrid metaheuristic model: Particle swarm optimization and radial movement optimization |
| title_full | Scenario-based wind speed estimation using a new hybrid metaheuristic model: Particle swarm optimization and radial movement optimization |
| title_fullStr | Scenario-based wind speed estimation using a new hybrid metaheuristic model: Particle swarm optimization and radial movement optimization |
| title_full_unstemmed | Scenario-based wind speed estimation using a new hybrid metaheuristic model: Particle swarm optimization and radial movement optimization |
| title_short | Scenario-based wind speed estimation using a new hybrid metaheuristic model: Particle swarm optimization and radial movement optimization |
| title_sort | scenario based wind speed estimation using a new hybrid metaheuristic model particle swarm optimization and radial movement optimization |
| url | https://doi.org/10.1177/0020294019842597 |
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