Wind speed forecasting using neural networks
| Autor: | Biswanath Samanta, Tyler Blanchard |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Renewable Energy Sustainability and the Environment Computer science 020209 energy Computer Science::Neural and Evolutionary Computation 020208 electrical & electronic engineering 0202 electrical engineering electronic engineering information engineering Energy Engineering and Power Technology 02 engineering and technology Time series Turbine Wind speed Marine engineering |
| Zdroj: | Wind Engineering. 44:33-48 |
| ISSN: | 2048-402X 0309-524X |
| DOI: | 10.1177/0309524x19849846 |
| Popis: | The prediction of wind speed is critical in the assessment of feasibility of a potential wind turbine site. This work presents a study on prediction of wind speed using artificial neural networks. Two variations of artificial neural networks, namely, nonlinear autoregressive neural network and nonlinear autoregressive neural network with exogenous inputs, were used to predict wind speed utilizing 1 year of hourly weather data from four locations around the United States to train, validate, and test these networks. This study optimized both neural network configurations and it demonstrated that both models were suitable for wind speed prediction. Both models outperformed persistence model (with a factor of about 2 to 10 in root mean square error ratio). Both artificial neural network models were implemented for single-step and multi-step-ahead prediction of wind speed for all four locations and results were compared. Nonlinear autoregressive neural network with exogenous inputs model gave better prediction performance than nonlinear autoregressive model and the difference was statistically significant. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|