Effects of environmental and turbine parameters on energy gains from wind farm system: Artificial neural network simulations
| Autor: | Aremu T Oriaje, Luqman K. Abidoye, Mamdouh El Haj Assad, Ehab Hussein Bani-Hani, Bassel Soudan, Mohammad Al-Shabi |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Renewable Energy Sustainability and the Environment Computer science 020209 energy Computer Science::Neural and Evolutionary Computation 0202 electrical engineering electronic engineering information engineering Energy Engineering and Power Technology 020201 artificial intelligence & image processing Control engineering 02 engineering and technology Turbine Energy (signal processing) |
| Zdroj: | Wind Engineering. 44:181-195 |
| ISSN: | 2048-402X 0309-524X |
| DOI: | 10.1177/0309524x19849834 |
| Popis: | Artificial neural network modelling has been employed to investigate the effects of various environmental and machine factors on the energy gain from wind farm systems. Numerical comparison of artificial neural network and nonlinear regression from XLSTAT showed that ANN possessed better numerical accuracy in predicting multivariate data. Several artificial neural network models are developed and tested with several structures to obtain the best prediction performance in energy gain from different wind farms in Jordan. The best performing artificial neural network model was used to predict the energy gain from wind farm based on changes in annual wind speed, turbine rotor diameter and turbine power. As a result of 20% increase in turbine power, 14.4%–31% energy gains were recorded across different wind farms. The proposed artificial neural network model was also a good predictor for energy cost resulting from specific wind farm design. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|