Comparison of Three Different Learning Methods of Multilayer Perceptron Neural Network for Wind Speed Forecasting
| Autor: | Mehmet BULUT, Hakan TORA, Magdi BUAISHA |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Multidisciplinary
Wind power Artificial neural network Energy management business.industry Computer science 020209 energy Mühendislik General Engineering 02 engineering and technology Industrial engineering Wind speed Renewable energy Engineering Electricity generation 0202 electrical engineering electronic engineering information engineering Wind speed forecasting Energy resources Artificial neural network Renewable energy 020201 artificial intelligence & image processing Electric power Energy supply business |
| Zdroj: | Volume: 34, Issue: 2 439-454 Gazi University Journal of Science |
| ISSN: | 2147-1762 |
| DOI: | 10.35378/gujs.764533 |
| Popis: | In the world, electric power is the highest need for high prosperity and comfortable living standards. The security of energy supply is an essential concept in national energy management. Therefore, ensuring the security of electricity supply requires accurate estimates of electricity demand. The share of electricity generation from renewables is significantly growing in the world. This kind of energy types are dependent on weather conditions as the wind and solar energies. There are two vital requirements to locate and measure specific systems to utilize wind power: modelling and forecasting of the wind velocity. To this end, using only 4 years of measured meteorological data, the present research attempts to estimate the related speed of wind within the Libyan Mediterranean coast with the help of ANN (artificial neural networking) with three different learning algorithms, which are Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient. Conclusions reached in this study show that wind speed can be estimated within acceptable limits using a limited set of meteorological data. In the results obtained, it was seen that the SCG algorithm gave better results in tests in this study with less data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|