Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models
| Autor: | Jorge Luis Tena García, Erasmo Cadenas Calderón, Gilberto González Ávalos, Eduardo Rangel Heras, Alain Mbikayi Tshikala |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Advances in Mechanical Engineering, Vol 11 (2019) |
| Druh dokumentu: | article |
| ISSN: | 1687-8140 16878140 |
| DOI: | 10.1177/1687814018813464 |
| Popis: | Forecast models for wind speed and wind turbine power generation are valuable support tools for operators of Control Energy Center. In this work, a year of daily energy output of a wind turbine is analyzed. The original time series was separated into a high-power sample and a low-power sample. High-power sample has a seasonal pattern while low-power sample does not. Afterward, a sARIMA model was produced for high-power sample forecast, with a good performance, while for low-power sample any ARIMA model defeated persistence model; thus, a couple of nonlinear autoregressive artificial neural networks are proposed. Mean absolute error and mean square error are reported and demonstrate that the sARIMA model can predict satisfactorily high-power sample, even with limited data, while to forecast low-power sample, it is necessary to use a neural networks approach and all data available to produce accurate forecasts. In each case, a normalized comparison with persistence model is also reported. Finally, a method which uses previous data of daily output energy and forecasted future wind speed values from a numeric weather prediction model is presented to objectively identify whether the current time is in a high-power or low-power regime to choose the ad hoc daily output energy forecast model. |
| Databáze: | Directory of Open Access Journals |
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